diff --git a/main.ipynb b/main.ipynb index bd9d1efc296ab37f38359d7c9d4f2280c9d647fc..438f12f04c14e04f317c49ece3b5e49a1868cde3 100644 --- a/main.ipynb +++ b/main.ipynb @@ -15,17 +15,16 @@ "| Name | Matriculation Number |\n", "|--------------------|----------------------|\n", "| Benjamin Jost | 11912846 |\n", - "| Julian Kienzel | |\n", + "| Julian Kienzl | 11908011 |\n", "| Fabio Maierbrugger | 11908625 |\n" ] }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ - "import json\n", "import textwrap\n", "import torch\n", "\n", @@ -59,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -120,7 +119,7 @@ "4 remember, pull your jaw off the floor after he..." ] }, - "execution_count": 61, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -263,7 +262,7 @@ "In get_sentiment_of_query we return a tuple that contains the sentiment and the sentiment score. Both of those values are returned by the sentiment-analysis pipeline. \n", "In get_sentiment_for_each_result we return a pd.dataframe that added both values(sentiment and the sentiment score) to a given pd.dataframe. For each of these values a new column is created within the pd.dataframe.\n", " \n", - "The higher the sentiment score is, the higher a text tends towards a sentiment. \n", + "The higher the sentiment score is, the more a text tends towards a sentiment.\n", " " ] }, @@ -301,12 +300,17 @@ "metadata": {}, "source": [ "### Evaluation of the Model\n", - "TODO: Description " + "\n", + "We evaluated the model by testing in on 1000 queries (reviews that the model is not trained on).\n", + "\n", + "The plots represent the following:\n", + " - The boxplot visualizes the distribution the sentiment percentages, broken down by each quartile.\n", + " - The bar charts represent the percentages of overlapping sentiment between the results and the query, while also denoting their found sentiment." ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ @@ -334,59 +338,29 @@ " ax.set_xticklabels(terms)\n", "\n", " plt.subplots_adjust(bottom=0.2)\n", + " plt.show()\n", + "\n", + "\n", + "def create_boxplot(percentages):\n", + " percentages = [percentages[x][2] for x in percentages]\n", + " plt.boxplot(percentages, vert=False)\n", + "\n", + " plt.xlabel('Percentage')\n", + " plt.ylabel('Data')\n", + " plt.title('Distribution')\n", + "\n", " plt.show()" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 82, "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[27], line 25\u001b[0m\n\u001b[1;32m 21\u001b[0m query_sentiment \u001b[38;5;241m=\u001b[39m bert_model\u001b[38;5;241m.\u001b[39mget_sentiment_of_query(query\u001b[38;5;241m=\u001b[39mquery\u001b[38;5;241m.\u001b[39mtext)\n\u001b[1;32m 22\u001b[0m query_results \u001b[38;5;241m=\u001b[39m bert_model\u001b[38;5;241m.\u001b[39mretrieve_top_k_entries_for_query(\n\u001b[1;32m 23\u001b[0m query\u001b[38;5;241m=\u001b[39mquery\u001b[38;5;241m.\u001b[39mtext, k\u001b[38;5;241m=\u001b[39mnumber_of_results\n\u001b[1;32m 24\u001b[0m )\n\u001b[0;32m---> 25\u001b[0m query_results_with_sentiment \u001b[38;5;241m=\u001b[39m \u001b[43mbert_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_sentiment_for_each_result\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[43m \u001b[49m\u001b[43mresults\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_results\u001b[49m\n\u001b[1;32m 27\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 29\u001b[0m sentiment_distribution \u001b[38;5;241m=\u001b[39m Counter(query_results_with_sentiment\u001b[38;5;241m.\u001b[39msentiment\u001b[38;5;241m.\u001b[39mtolist())\n\u001b[1;32m 30\u001b[0m percentage \u001b[38;5;241m=\u001b[39m (sentiment_distribution[query_sentiment[\u001b[38;5;241m0\u001b[39m]] \u001b[38;5;241m/\u001b[39m number_of_results) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m\n", - "Cell \u001b[0;32mIn[15], line 33\u001b[0m, in \u001b[0;36mBERT.get_sentiment_for_each_result\u001b[0;34m(self, results)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_sentiment_for_each_result\u001b[39m(\u001b[38;5;28mself\u001b[39m, results: pd\u001b[38;5;241m.\u001b[39mDataFrame) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame:\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m index, row \u001b[38;5;129;01min\u001b[39;00m results\u001b[38;5;241m.\u001b[39miterrows():\n\u001b[0;32m---> 33\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msentiment_analyzer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 34\u001b[0m results\u001b[38;5;241m.\u001b[39mloc[index, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msentiment\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m r[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m results\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/transformers/pipelines/text_classification.py:156\u001b[0m, in \u001b[0;36mTextClassificationPipeline.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 123\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;124;03m Classify the text(s) given as inputs.\u001b[39;00m\n\u001b[1;32m 125\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[38;5;124;03m If `top_k` is used, one such dictionary is returned per label.\u001b[39;00m\n\u001b[1;32m 155\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 156\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 157\u001b[0m \u001b[38;5;66;03m# TODO try and retrieve it in a nicer way from _sanitize_parameters.\u001b[39;00m\n\u001b[1;32m 158\u001b[0m _legacy \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtop_k\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m kwargs\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/transformers/pipelines/base.py:1140\u001b[0m, in \u001b[0;36mPipeline.__call__\u001b[0;34m(self, inputs, num_workers, batch_size, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1132\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(\n\u001b[1;32m 1133\u001b[0m \u001b[38;5;28miter\u001b[39m(\n\u001b[1;32m 1134\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_iterator(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1137\u001b[0m )\n\u001b[1;32m 1138\u001b[0m )\n\u001b[1;32m 1139\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1140\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_single\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreprocess_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforward_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpostprocess_params\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/transformers/pipelines/base.py:1147\u001b[0m, in \u001b[0;36mPipeline.run_single\u001b[0;34m(self, inputs, preprocess_params, forward_params, postprocess_params)\u001b[0m\n\u001b[1;32m 1145\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun_single\u001b[39m(\u001b[38;5;28mself\u001b[39m, inputs, preprocess_params, forward_params, postprocess_params):\n\u001b[1;32m 1146\u001b[0m model_inputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpreprocess(inputs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpreprocess_params)\n\u001b[0;32m-> 1147\u001b[0m model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mforward_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1148\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpostprocess(model_outputs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpostprocess_params)\n\u001b[1;32m 1149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outputs\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/transformers/pipelines/base.py:1046\u001b[0m, in \u001b[0;36mPipeline.forward\u001b[0;34m(self, model_inputs, **forward_params)\u001b[0m\n\u001b[1;32m 1044\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m inference_context():\n\u001b[1;32m 1045\u001b[0m model_inputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ensure_tensor_on_device(model_inputs, device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m-> 1046\u001b[0m model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mforward_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1047\u001b[0m model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ensure_tensor_on_device(model_outputs, device\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mdevice(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m 1048\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/transformers/pipelines/text_classification.py:187\u001b[0m, in \u001b[0;36mTextClassificationPipeline._forward\u001b[0;34m(self, model_inputs)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muse_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39msignature(model_forward)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m 186\u001b[0m model_inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muse_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 187\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/transformers/models/distilbert/modeling_distilbert.py:1000\u001b[0m, in \u001b[0;36mDistilBertForSequenceClassification.forward\u001b[0;34m(self, input_ids, attention_mask, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 992\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 993\u001b[0m \u001b[38;5;124;03mlabels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\u001b[39;00m\n\u001b[1;32m 994\u001b[0m \u001b[38;5;124;03m Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,\u001b[39;00m\n\u001b[1;32m 995\u001b[0m \u001b[38;5;124;03m config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If\u001b[39;00m\n\u001b[1;32m 996\u001b[0m \u001b[38;5;124;03m `config.num_labels > 1` a classification loss is computed (Cross-Entropy).\u001b[39;00m\n\u001b[1;32m 997\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 998\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[0;32m-> 1000\u001b[0m distilbert_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdistilbert\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1001\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1002\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1003\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1004\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1005\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1006\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1007\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1008\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1009\u001b[0m hidden_state \u001b[38;5;241m=\u001b[39m distilbert_output[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;66;03m# (bs, seq_len, dim)\u001b[39;00m\n\u001b[1;32m 1010\u001b[0m pooled_output \u001b[38;5;241m=\u001b[39m hidden_state[:, \u001b[38;5;241m0\u001b[39m] \u001b[38;5;66;03m# (bs, dim)\u001b[39;00m\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/transformers/models/distilbert/modeling_distilbert.py:820\u001b[0m, in \u001b[0;36mDistilBertModel.forward\u001b[0;34m(self, input_ids, attention_mask, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 817\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attention_mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 818\u001b[0m attention_mask \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mones(input_shape, device\u001b[38;5;241m=\u001b[39mdevice) \u001b[38;5;66;03m# (bs, seq_length)\u001b[39;00m\n\u001b[0;32m--> 820\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransformer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 821\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43membeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 822\u001b[0m \u001b[43m \u001b[49m\u001b[43mattn_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 823\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 824\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 825\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 826\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 827\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/transformers/models/distilbert/modeling_distilbert.py:585\u001b[0m, in \u001b[0;36mTransformer.forward\u001b[0;34m(self, x, attn_mask, head_mask, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 577\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(\n\u001b[1;32m 578\u001b[0m layer_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m,\n\u001b[1;32m 579\u001b[0m hidden_state,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 582\u001b[0m output_attentions,\n\u001b[1;32m 583\u001b[0m )\n\u001b[1;32m 584\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 585\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mlayer_module\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 586\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 587\u001b[0m \u001b[43m \u001b[49m\u001b[43mattn_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 588\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 589\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 590\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 592\u001b[0m hidden_state \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 594\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/transformers/models/distilbert/modeling_distilbert.py:530\u001b[0m, in \u001b[0;36mTransformerBlock.forward\u001b[0;34m(self, x, attn_mask, head_mask, output_attentions)\u001b[0m\n\u001b[1;32m 528\u001b[0m \u001b[38;5;66;03m# Feed Forward Network\u001b[39;00m\n\u001b[1;32m 529\u001b[0m ffn_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mffn(sa_output) \u001b[38;5;66;03m# (bs, seq_length, dim)\u001b[39;00m\n\u001b[0;32m--> 530\u001b[0m ffn_output: torch\u001b[38;5;241m.\u001b[39mTensor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput_layer_norm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mffn_output\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43msa_output\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# (bs, seq_length, dim)\u001b[39;00m\n\u001b[1;32m 532\u001b[0m output \u001b[38;5;241m=\u001b[39m (ffn_output,)\n\u001b[1;32m 533\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/nn/modules/normalization.py:196\u001b[0m, in \u001b[0;36mLayerNorm.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 196\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayer_norm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 197\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalized_shape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/nn/functional.py:2543\u001b[0m, in \u001b[0;36mlayer_norm\u001b[0;34m(input, normalized_shape, weight, bias, eps)\u001b[0m\n\u001b[1;32m 2539\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_variadic(\u001b[38;5;28minput\u001b[39m, weight, bias):\n\u001b[1;32m 2540\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 2541\u001b[0m layer_norm, (\u001b[38;5;28minput\u001b[39m, weight, bias), \u001b[38;5;28minput\u001b[39m, normalized_shape, weight\u001b[38;5;241m=\u001b[39mweight, bias\u001b[38;5;241m=\u001b[39mbias, eps\u001b[38;5;241m=\u001b[39meps\n\u001b[1;32m 2542\u001b[0m )\n\u001b[0;32m-> 2543\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayer_norm\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnormalized_shape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackends\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcudnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menabled\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ - "# queries = [\n", - "# \"Cruel and Unusual is the first Patricia Cornwell book I have got read and I for one loved it and I can't wait the read more of her books my boyfriend told me about Patricia Cornwell books he said I need to read Hornet's Nest and Southern Cross and those will be my next two book I will be reading and I will be reading them on my new IPad I got for Christmas I can't wait keep up the great writing Patricia and thanks to my boyfriend for telling me about this awesome writer.........RKsbabydoll\",\n", - "# \"Actually a good TV. Unfortunately, after a few months the picture is only white. It also cannot be adjusted. The television is therefore junk.\",\n", - "# \"I don't really understand the positive reviews here. Yes, the RGB light is good and makes it easier to use in a dark room. The additional buttons are also practical... but seriously...What year is it? The keys on this keyboard remind me of my C64 breadbox: see high and loud keystrokes. The feeling is 1:1 the same.I like flat keyboards myself and still gave this Logitech a chance. But after 30 minutes it was over... I'll definitely never get used to that! Therefore, unfortunately, a reach into the toilet.If there ever is a flat version with a quiet stop, I would be happy to test it again.\",\n", - "# \"I recently got the [Laptop Brand/Model] and it's been a game-changer. The sleek design caught my eye, and it performs like a champ—smooth multitasking, vibrant display. Battery life is decent, lasting through my workday. Overall, a solid buy for the price!\",\n", - "# \"Purchased the Lenovo Notebook, and it's been a reliable companion. The design is sleek, and it handles tasks effortlessly. Impressed with the decent battery life, making it suitable for daily use. Overall, a good value for the money.\"\n", - "# ]\n", "test_dataset = pd.read_csv(\n", " \"data/train_short_200000.csv\", names=[\"sentiment\", \"title\", \"text\"], sep=\",\"\n", - ").tail(100)\n", + ").tail(1000)\n", "\n", "test_dataset = test_dataset.dropna()\n", "test_dataset = test_dataset.drop([\"title\"], axis=1)\n", @@ -411,32 +385,27 @@ " textwrap.shorten(query.text, width=30),\n", " query_sentiment,\n", " percentage,\n", - " )\n", - "\n", - "# print(json.dumps(percentages, indent=4))\n", - "\n", - "\n", - "# TODO what to do with all these percentages? How to evaluate/plot them?" + " )" ] }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 103, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Highest positive query: ('very nice movie , it [...]', ('POSITIVE', 0.9998714923858643), 90.0)\n", - "Highest negative query: ('i am returning my copy. [...]', ('NEGATIVE', 0.9997848868370056), 94.0)\n", - "Lowest positive query: ('this was so terrible, i [...]', ('POSITIVE', 0.997624933719635), 6.0)\n", - "Lowest negative query: (\"i'm preparing the capes [...]\", ('NEGATIVE', 0.5031839609146118), 22.0)\n" + "Highest positive query: ('one of the best albums i [...]', ('POSITIVE', 0.9998667240142822), 96.0)\n", + "Highest negative query: ('ordered this dual pack. [...]', ('NEGATIVE', 0.9997628331184387), 98.0)\n", + "Lowest positive query: ('this was so terrible, i [...]', ('POSITIVE', 0.9799081087112427), 6.0)\n", + "Lowest negative query: ('uncle albert, which you [...]', ('NEGATIVE', 0.9958155751228333), 16.0)\n" ] }, { "data": { - "image/png": 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U7uTJk+jatStMTU1RqVIl1KtXD0uWLEFubq5Su4iICHTo0AFVqlSRrifl5+eHly9fKu3v2bNnGDx4sNK+37zlX+X89ce3Xr16CAgIKPRYfv/9d+jq6uLRo0cFjuv1W0pKyjs9ZsHBwWjZsiVMTExgZGSEOnXqyOqNwfbt29GwYUPo6+vD3t4e33//fYE2P/30E2rVqgU9PT24uLhgw4YNb+03LCwMrVq1gpGRESwtLfHll18iJydHqc3BgwfRsmVLGBkZwczMDD4+PoiPj5fWF/WcqFOnjtTm2LFj6N69O6ytraFQKBASElKglh9++AHm5uYwNzfH4sWLldadPn0aTZo0KVCbXDHIqNnt27fRtGlTxMbGYsuWLbh58yYCAwMRFhYGNzc3PHnypFz3n//iWhGVZ21VqlQp8I2pHwJNTU1YWlpCS6t8vyLKxMQElStXVmnb/Bd5hUKh8v7zj9PQ0FDlPt4UHR2NpKQk7NixA9euXUP37t2lkBIcHAx3d3fY2Njg6NGjuHHjBsaPH49vv/0W/fr1k77r5Z9//oGXlxeaNm2KY8eO4cqVK1ixYgV0dHQKBB7gVSBMTk6WbgAQFBQk3T979myBbfz9/bF161b8+++/BdYFBQXh008/RbVq1ZSO6/V9JCcnw9zcXOXHKSwsDH379oWPjw/OnDmD8+fP47vvvkN2drbKfb5Nbm4u8vLyyqSv/fv3w9fXFwEBAbh69Sp+/vlnLF26FCtXrpTarFq1CtOmTcOsWbNw7do1zJ49G2PGjMHu3buL7Pfy5cvo2rUrvLy8cPHiRWzbtg2hoaH46quvpDZxcXHw9vZGhw4dcOnSJRw8eBCPHj1Cr169pDZvPifu3LmDKlWqoE+fPlKbjIwMNGjQAD/99FOhtURFRWHGjBnYunUrtmzZgm+++QZXrlwBAOTk5CAgIACBgYHl/jrx3qj34tvk5eUlbGxsxIsXL5SWJycnC319fREQECAtAyCCg4OV2pmYmIigoCDpfmJioujTp48wMTERpqam4tNPPxVxcXHSej8/P+Ht7S2+/fZbYWVlJRwcHMTs2bNFnTp1CtTWoEED8c0337z1GA4ePCh0dXXF06dPlZaPGzdOtG/fXrofGRkp2rRpIypVqiRsbGzEF198IdLT06X19vb2Ys6cOWLgwIHCyMhI+Pn5ifbt24sxY8Yo9fvgwQOhra0tjhw5Umg9M2fOFA0aNBAbNmwQ9vb2wtjYWPTt21ekpaVJbdzd3cX48eOl+5mZmWLq1KnCxsZG6OjoCEdHR/Hrr79K669cuSK8vLyEgYGBMDc3F59//rl4+PBhkY9JUFCQMDExEbt37xbOzs5CT09P+Pj4iIyMDLFu3Tphb28vKleuLL744guRk5MjbffkyRMxcOBAUblyZaGnpye8vLxETEyMEEKI1NRUUalSJbFv3z6lfe3cuVMYGhqKjIwMERcXJwCIixcvqlx7SeQ/j/J/BqB0e/059zp7e3uxdOnSd9r36/J/18Wxt7cXR48eLXL90aNHBQCl5++mTZsEAHHjxg2Rnp4uqlatKnr16lVg29DQUAFAbN26VQghxNKlS4WDg0Ox9RS2v3yF/Y0Lofx8ffjwodDR0RG///67Upvbt28LhUIh9u/f/9b9FMfd3V3pNeVN48ePF+3atXtrP6GhoaJp06ZCV1dXVK1aVfTo0UNaV9zzXIj/9/eza9cuUatWLaGpqSni4uJEZmammDx5srC2thb6+vqiefPmxf5uC9O/f3/Ru3dvpWXLly8XNjY2Ii8vTwghhJubm5gyZYpSm0mTJonWrVsX2e+0adNE06ZNCzwGlSpVkl57duzYIbS0tERubq5SG4VCIV6+fFlov8HBwUKhUIj4+PhC1xf2nNm2bZto0aKFdL958+Zi+/btQggh5s2bJ8aNG1fkccgRR2TU6MmTJzh48CBGjx4NPT09pXWWlpbw9fXFtm3bSvzNntnZ2fD09ISRkREiIyNx4sQJGBoawsvLS2l0IywsDNHR0Th8+DD27NmDoUOH4vr160rv/i5evIioqCgMGTJEGqJ+ffjzdR07dkTlypXx559/Sstyc3Oxbds2+Pr6AgBu3boFLy8v+Pj4ICoqCtu2bcPx48cxduxYpb5++OEHNGjQABcvXsT06dMxbNgwbN68GVlZWVKbjRs3onr16ujQoUORj8WtW7cQEhKCPXv2YM+ePYiIiMCCBQuKbD9o0CBs2bIFy5cvx/Xr17F69Wrp3f6zZ8/QoUMHNGrUCOfOncOBAwdw//59fPbZZ0X2BwAvXrzA8uXLsXXrVhw4cADh4eHo2bMn9u3bh3379uH333/H6tWr8ccff0jbDB48GOfOnUNoaChOnToFIQS6du2K7OxsGBsb45NPPsHmzZuV9rNp0yb06NED+vr6BWpQtfbS+PHHH+Hm5obhw4dL7yJtbW3LrH91yP97fPnyJQ4dOoTHjx9jypQpBdp1794dzs7O2LJlC4BXf7fJyck4duxYudVWrVo1eHt747ffflNavm7dOtjY2KBz587ltm/g1TFeu3YNV69eLbLN3r170bNnT3Tt2hUXL15EWFiY0oUui3ue53vx4gUWLlyIX3/9FdeuXYO5uTnGjh2LU6dOYevWrYiKikKfPn3g5eWF2NhYaTuFQlHsHJ+srCxUqlRJaZmenh7u3r2LhISEYtucOXOmyJGnorbJzMzE+fPnAQBNmjSBhoYGgoKCkJubi9TUVPz+++/w8PCAtrZ2of2uXbsWHh4esLe3L/KY3lSvXj3ExMQgMTERCQkJiImJQd26dXHr1i0EBQXh22+/LXFfsqDmIPWf9vfffxf5DkwIIZYsWSIAiPv37wsh3j4i8/vvvwsXFxfpXYUQQmRlZQk9PT1x8OBBIcSrd88WFhYiKytLqZ8uXbqIUaNGSfe/+OIL6V3X6dOnhYuLi7h7926RxzJ+/HjRoUMH6f6bozT+/v5ixIgRSttERkYKDQ0N8e+//wohXr1zfv1dmxBC/Pvvv8LU1FRs27ZNWla/fn0xa9asImuZOXOm0NfXVxqB+d///qf0DuX1d7jR0dECgDh8+HCh/c2dO1d07txZadmdO3cEABEdHV3oNkFBQQKAuHnzprRs5MiRQl9fXzx//lxa5unpKUaOHCmEECImJkYAECdOnJDWP3r0SOjp6UnvpoKDg6XRFyH+3yhN/rvwN0dkVKm9JF4fkRGi4AhXUeQwIpOUlCRatWolqlevLrKyssSCBQuKHdn49NNPRa1atYQQQuTk5IjBgwcLAMLS0lL06NFDrFixQqSmpha5v9cV9Xrw5uN74MABoVAoxO3bt4UQQuTl5Ql7e3ulEdT8/RgYGCjdateuXeRjkb+v4kZk0tPTRdeuXQUAYW9vL/r27SvWrl0rMjMzpTZubm7C19e30O1L8jzP//u5dOmS1CYhIUFoamqKe/fuKfXXsWNHMW3aNOm+i4uL2LlzZ5H1r169Wujr64sjR46I3NxcER0dLVxdXQUAcfLkSSHEq9EVS0tLce7cOZGXlyfOnj0rLCwsBACRlJRUaL8HDx4UGhoaYvPmzSInJ0fcvXtXtG3bVgAQmzdvltqFh4cLc3NzoampKQAINze3Ip9b9+7dE5qamkqvf28q6jmzatUq4ezsLJydncWqVaukxyo4OFjs2LFD1KlTRzRs2FBEREQU2bdccESmAhBvGXHR0dEpUT+XL1/GzZs3YWRkBENDQxgaGqJKlSrIzMzErVu3pHb16tUr0Ofw4cOxZcsWZGZm4uXLl9i8eTOGDh0KAGjevDlu3LiB6tWrF7lvX19fhIeHIykpCcCrUYJu3bpJ8yguX76MdevWSXUZGhrC09MTeXl5iIuLk/pp2rSpUr+VKlXCwIEDpXefFy5cwNWrVzF48OBiHwsHBwelOTBWVlZ48OBBoW0vXboETU1NuLu7F7r+8uXLOHr0qFLtrq6uAKD0uL5JX18fjo6O0n0LCws4ODgozeuwsLCQ6rp+/Tq0tLTQokULaX3VqlXh4uKC69evAwC6du0KbW1thIaGAgD+/PNPGBsbw8PDo0xr/y+ysbGBgYEBrK2tkZGRgT///FPp7+Rtf6fAq7k7QUFBuHv3LhYtWoTq1atj3rx5qFOnjjQHpix06tQJNjY2CAoKAvBqlDUxMRFDhgwp0DYyMhKXLl2Sbvv27XunfRsYGGDv3r24efMmvvnmGxgaGmLy5Mlo3rw5Xrx4AeDV31THjh0L3b4kz3Pg1ete/fr1pftXrlxBbm4unJ2dlZ7PERERSs/lGzduoGfPnkXWP3z4cIwdOxaffPIJdHR00LJlS/Tr1w8AoKHx6l/i9OnT0aVLF7Rs2RLa2trw9vaGn5+fUps3de7cGd9//z0CAgKgq6sLZ2dndO3aVWmblJQUDB8+HH5+fjh79iwiIiKgo6OD3r17F/r8Wr9+PSpXrqzSGYIBAQGIjo5GdHQ0AgICsH79ehgZGcHNzQ3Dhg1DcHAwlixZgn79+imNeMvRBzLTR55q1qwJhUKB69evF/qHd/36dZiZmUlhQKFQFHiyvz7MmZ6ejiZNmmDTpk0F+jIzM5N+NjAwKLC+e/fu0NXVRXBwMHR0dJCdnY3evXuX+FiaNWsGR0dHbN26FaNGjUJwcLDS8G56ejpGjhyJcePGFdjWzs6u2NqGDRuGhg0b4u7duwgKCkKHDh3eOsz65jCtQqEocrLgmx/rvSk9PR3du3fHwoULC6yzsrIqVQ2lqasw+S96mzdvRr9+/bB582b07du3yEl7qtb+XxQZGQljY2OYm5srhWBnZ2cAr/4eW7VqVWC769evo3bt2krLqlevjoEDB2LgwIGYO3cunJ2dERgYiNmzZ5dJrRoaGhg8eDDWr1+PWbNmISgoCO3bt8dHH31UoG2NGjVUnphdHEdHRzg6OmLYsGH4+uuv4ezsjG3btmHIkCFv/ZsqCT09PaUJ4enp6dDU1MT58+ehqamp1LY0k74VCgUWLlyIefPmISUlBWZmZggLCwMA6fHT09PDb7/9htWrV+P+/fuwsrLCmjVrpDONijJp0iRMnDgRycnJMDU1RXx8PKZNmyb1+9NPP8HExASLFi2Sttm4cSNsbW1x+vRptGzZUlouhMBvv/2GgQMHlvjNbFEePXqE2bNn49ixYzh9+jScnZ3h5OQEJycnZGdnIyYmBvXq1XunfagTg4waVa1aFZ06dcLPP/+MiRMnKv3xp6SkYNOmTRgzZoy0zMzMTOldXWxsrPQOCAAaN26Mbdu2wdzcHMbGxqWqRUtLC35+fggKCoKOjg769etX6hcjX19fbNq0CTY2NtDQ0EC3bt2Uavvnn39Qs2bNUvUJvBpBatq0KX755Rds3rxZ6eyCslCvXj3k5eUhIiKi0JGNxo0b488//4SDg0O5zvKvVasWcnJycPr0aekf5uPHjxEdHa30j9LX1xedOnXCtWvX8NdffxX7eff7qr2os3LkpKh/+J07d0aVKlWwePHiAkEmNDQUsbGxxZ7Kb2pqCisrK2RkZJRpvUOGDMG3336LnTt3Ijg4GL/++muZ9l8aDg4O0NfXl46xfv36CAsLK3SEqKTP8zc1atQIubm5ePDgAdq2bfvONWtqakqjzFu2bIGbm1uBkKKtrQ0bGxsAwNatW/HJJ58UOSKTT6FQwNraWurX1tYWjRs3BvBq3s+b2+eHsjff0ERERODmzZvw9/dX8Qj/n4kTJ2LixImwsbHB2bNnld4A5+TkyP5vlx8tqdnKlSuRlZUFT09PHDt2DHfu3MGBAwfQqVMnODs7Y8aMGVLbDh06YOXKlbh48SLOnTuHgIAApXf4vr6+0kTAyMhIxMXFITw8HOPGjcPdu3ffWsuwYcPw119/4cCBA9LHSgBw5swZuLq64t69e8Vu7+vriwsXLuC7775D7969oaurK6378ssvcfLkSYwdOxaXLl1CbGwsdu3aVWCyb3G1LViwAEKIYoeNVeHg4AA/Pz8MHToUISEh0uO2fft2AMCYMWPw5MkT9O/fH2fPnsWtW7dw8OBBDBkypExfAJycnODt7Y3hw4fj+PHjuHz5Mj7//HNUr14d3t7eUruPP/5Ymgxeo0YNpSH6N72v2h0cHHD69GnEx8fj0aNHpRplunfvHlxdXXHmzBlp2aBBgzBt2jTpfnBwsPSR2PtmYGCA1atXY9euXRgxYgSioqIQHx+PtWvXYvDgwejdu7c0eXr16tUYNWoUDh06hFu3buHatWv48ssvpdO5y1KNGjXQoUMHjBgxArq6ukqn8L7uwYMHSElJUbq9y6nSs2bNwtSpUxEeHo64uDhcvHgRQ4cORXZ2Njp16gQAmDlzJrZs2YKZM2fi+vXruHLlijQqWNLn+ZucnZ3h6+uLQYMGYefOnYiLi8OZM2cwf/587N27V2rn6uqK4ODgIvt59OgRAgMDcePGDVy6dAnjx4/Hjh07sGzZMqlNTEwMNm7ciNjYWJw5cwb9+vXD1atXMW/ePKlNYc/J77//HleuXMG1a9cwd+5cLFiwAMuXL5fCSrdu3XD27FnMmTMHsbGxuHDhAoYMGQJ7e3s0atRIqa+1a9eiRYsWhX4nVHp6uvRRIfDqtO5Lly4hMTGxQNvDhw8jJiZGelPcrFkz3LhxA/v378eaNWugqakJFxeXIh8vOWCQUTMnJyecPXsWH330ET777DPY29ujS5cucHZ2ls46yrd48WLY2tqibdu2GDBgAKZMmaJ0poq+vj6OHTsGOzs79OrVC7Vq1YK/vz8yMzNLNELj5OSEVq1awdXVVemf44sXLxAdHf3WF7+aNWuiefPmiIqKks5Wyle/fn1EREQgJiYGbdu2RaNGjTBjxgzpncvb9O/fH1paWujfv3+BMwPKwqpVq9C7d2+MHj0arq6uGD58uPTu0traGidOnEBubi46d+6MevXqYcKECahcufJb352VVlBQEJo0aYJPPvkEbm5uEEJg3759SoFVoVCgf//+uHz5coHH+U2q1v62Mz/eNGXKFGhqaqJ27dowMzMr9AW1KNnZ2YiOjlYaXUxMTFQafUxNTUV0dHSJ+yxrvXv3xtGjR5GYmIi2bdvCxcUFS5cuxddff42tW7dKH4E0b94c6enpCAgIQJ06deDu7o6///4bISEhRc7Behf+/v54+vQpBgwYUOTfhYuLC6ysrJRu+WfRqMLd3R23b9/GoEGD4Orqii5duiAlJQWHDh2S/iG2a9cOO3bsQGhoKBo2bIgOHTooBdWSPM8LExQUhEGDBmHy5MlwcXFBjx49cPbsWaWPp6Ojo5GamlpsP+vXr0fTpk3RunVrXLt2DeHh4UpnVeXm5mLx4sVo0KABOnXqhMzMTJw8eVL6gkKg8Ofk/v370bZtWzRt2hR79+7Frl27lOa3dOjQAZs3b0ZISAgaNWoELy8v6Orq4sCBA0oj4Kmpqfjzzz+LHI05d+4cGjVqJIWfSZMmSa+pr/v3338xduxYrF69Wvp7t7GxwYoVKzBkyBB89913WL9+fZl8FKhW6ptnTEWZMWOGMDQ0FKdOnXqv+83LyxOOjo5i8eLF73W/JREXFyc0NDTE+fPn1V3KB+/27dtCS0tL6Xs9ykpFPGuJlL3trCWiioYjMhXQ7NmzsXz5cvz9999l9m2Wb/Pw4UOsXLkSKSkphX6urS7Z2dlISUnBN998g5YtW0qfNVP52bdvH0aMGAEnJ6dy6f/LL7+EoaHhW981FycxMRGGhoZKQ/1E9N/Eyb4V1PsOE+bm5qhWrRrWrFkDU1PT97rv4pw4cQLt27eHs7Oz0hfHUfl5fYJ5WYuIiJA+onyXS0RYW1tL8wNen4tFRP89CiFK+LWxREQytGzZMvTo0UNpfgMVbd26dWjYsOFbrypOVFEwyBAREZFscY4MERERydYHP0cmLy8PSUlJMDIyUvqWSCIiIqq4hBB4/vw5rK2ti/26iA8+yCQlJcn+SrxERET/VXfu3JG+YbkwH3yQyT8z4s6dO6X+2n4iIiJSj7S0NNja2r71DMcPPsjkf5xkbGzMIENERCQzb5sWwsm+VO4OHDiApk2bon79+mjZsiUuX74M4NU1YLy8vODk5IS6devi2LFjRfaxZ88euLq6wsnJCb169UJaWtr7Kp+IiCowBhkqV0+fPoWvry/Wr1+PqKgofP/999L1gb766iu0bNkSsbGxCAoKwoABAwq9nlN6ejr8/f0REhKC2NhYWFtbF3u1YSIi+u9gkKFydevWLVStWhV16tQBALRt2xaJiYm4cOECtm/fjoCAAACvrshqbW2NiIiIAn3s378fjRo1kq40O3r0aGzZsuX9HQQREVVYDDJUrpycnPD48WOcPHkSABAaGornz58jLi4O2dnZsLS0lNo6ODgUetXkxMRE2NvbK7VLTk5GTk5O+R8AERFVaB/8ZF9SLxMTE/zxxx+YNm0a0tPT4ebmhtq1ayM9PV3dpRER0QeAQYbKXfv27dG+fXsAQFZWFiwtLdG6dWtoaWkhJSVFGpWJj4+HnZ1dge3t7Oxw+PBh6X58fDysrKygpcWnLxHRfx0/WqJyl5ycLP08d+5cdOjQATVr1kSfPn0QGBgIADh79izu3bsHd3f3Att7eXnhwoULuHHjBgDg559/Rr9+/d5P8UREVKHxLS2VuxkzZiAyMhI5OTlwc3PD2rVrAQALFy7EwIED4eTkBB0dHWzcuBHa2trSNtbW1ggICICRkRF+/fVX9OjRAzk5Oahbty7Wr1+vzkMiIqIK4oO/+nVaWhpMTEyQmprKL8QjIiKSiZL+/+ZHS0RERCRbDDJEREQkWwwyREREJFsMMkRERCRbPGvpHShmF39FTqKKQMz8oOfzE9F/HEdkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIttQaZHJzczF9+nTUqFEDenp6cHR0xNy5cyGEkNoIITBjxgxYWVlBT08PHh4eiI2NVWPVREREVFGoNcgsXLgQq1atwsqVK3H9+nUsXLgQixYtwooVK6Q2ixYtwvLlyxEYGIjTp0/DwMAAnp6eyMzMVGPlREREVBFoqXPnJ0+ehLe3N7p16wYAcHBwwJYtW3DmzBkAr0Zjli1bhm+++Qbe3t4AgA0bNsDCwgIhISHo169fgT6zsrKQlZUl3U9LS3sPR0JERETqoNYRmVatWiEsLAwxMTEAgMuXL+P48ePo0qULACAuLg4pKSnw8PCQtjExMUGLFi1w6tSpQvucP38+TExMpJutrW35HwgRERGphVpHZL766iukpaXB1dUVmpqayM3NxXfffQdfX18AQEpKCgDAwsJCaTsLCwtp3ZumTZuGSZMmSffT0tIYZoiIiD5Qag0y27dvx6ZNm7B582bUqVMHly5dwoQJE2BtbQ0/Pz+V+tTV1YWurm4ZV0pEREQVkVqDzP/+9z989dVX0lyXevXqISEhAfPnz4efnx8sLS0BAPfv34eVlZW03f3799GwYUN1lExEREQViFrnyLx48QIaGsolaGpqIi8vDwBQo0YNWFpaIiwsTFqflpaG06dPw83N7b3WSkRERBWPWkdkunfvju+++w52dnaoU6cOLl68iCVLlmDo0KEAAIVCgQkTJuDbb7+Fk5MTatSogenTp8Pa2ho9evRQZ+lERERUAag1yKxYsQLTp0/H6NGj8eDBA1hbW2PkyJGYMWOG1Gbq1KnIyMjAiBEj8OzZM7Rp0wYHDhxApUqV1Fg5ERERVQQK8frX6H6A0tLSYGJigtTUVBgbG5dp34rZijLtj6g8iJkf9J84EX2gSvr/m9daIiIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlSe5C5d+8ePv/8c1StWhV6enqoV68ezp07J60XQmDGjBmwsrKCnp4ePDw8EBsbq8aKiYiIqKJQa5B5+vQpWrduDW1tbezfvx///PMPFi9eDFNTU6nNokWLsHz5cgQGBuL06dMwMDCAp6cnMjMz1Vg5ERERVQRa6tz5woULYWtri6CgIGlZjRo1pJ+FEFi2bBm++eYbeHt7AwA2bNgACwsLhISEoF+/fgX6zMrKQlZWlnQ/LS2tHI+AiIiI1EmtIzKhoaFo2rQp+vTpA3NzczRq1Ai//PKLtD4uLg4pKSnw8PCQlpmYmKBFixY4depUoX3Onz8fJiYm0s3W1rbcj4OIiIjUQ61B5vbt21i1ahWcnJxw8OBBjBo1CuPGjcP69esBACkpKQAACwsLpe0sLCykdW+aNm0aUlNTpdudO3fK9yCIiIhIbdT60VJeXh6aNm2KefPmAQAaNWqEq1evIjAwEH5+fir1qaurC11d3bIsk4iIiCootY7IWFlZoXbt2krLatWqhcTERACApaUlAOD+/ftKbe7fvy+tIyIiov8utQaZ1q1bIzo6WmlZTEwM7O3tAbya+GtpaYmwsDBpfVpaGk6fPg03N7f3WisRERFVPGr9aGnixIlo1aoV5s2bh88++wxnzpzBmjVrsGbNGgCAQqHAhAkT8O2338LJyQk1atTA9OnTYW1tjR49eqizdCIiIqoA1BpkmjVrhuDgYEybNg1z5sxBjRo1sGzZMvj6+kptpk6dioyMDIwYMQLPnj1DmzZtcODAAVSqVEmNlRMREVFFoBBCCHUXUZ7S0tJgYmKC1NRUGBsbl2nfitmKMu2PqDyImR/0nzgRfaBK+v9b7ZcoICIiIlIVgwwRERHJFoMMERERyRaDDBEREcmWSkHm9u3bZV0HERERUampFGRq1qyJ9u3bY+PGjcjMzCzrmoiIiIhKRKUgc+HCBdSvXx+TJk2CpaUlRo4ciTNnzpR1bURERETFUinINGzYED/++COSkpLw22+/ITk5GW3atEHdunWxZMkSPHz4sKzrJCIiIirgnSb7amlpoVevXtixYwcWLlyImzdvYsqUKbC1tcWgQYOQnJxcVnUSERERFfBOQebcuXMYPXo0rKyssGTJEkyZMgW3bt3C4cOHkZSUBG9v77Kqk4iIiKgAla61tGTJEgQFBSE6Ohpdu3bFhg0b0LVrV2hovMpFNWrUwLp16+Dg4FCWtRIREREpUSnIrFq1CkOHDsXgwYNhZWVVaBtzc3OsXbv2nYojIiIiKo5KQSY2NvatbXR0dODn56dK90REREQlotIcmaCgIOzYsaPA8h07dmD9+vXvXBQRERFRSagUZObPn49q1aoVWG5ubo558+a9c1FEREREJaFSkElMTESNGjUKLLe3t0diYuI7F0VERERUEioFGXNzc0RFRRVYfvnyZVStWvWdiyIiIiIqCZWCTP/+/TFu3DgcPXoUubm5yM3NxV9//YXx48ejX79+ZV0jERERUaFUOmtp7ty5iI+PR8eOHaGl9aqLvLw8DBo0iHNkiIiI6L1RKcjo6Ohg27ZtmDt3Li5fvgw9PT3Uq1cP9vb2ZV0fERERUZFUCjL5nJ2d4ezsXFa1EBEREZWKSkEmNzcX69atQ1hYGB48eIC8vDyl9X/99VeZFEdERERUHJWCzPjx47Fu3Tp069YNdevWhUKhKOu6iIiIiN5KpSCzdetWbN++HV27di3reoiIiIhKTKXTr3V0dFCzZs2yroWIiIioVFQKMpMnT8aPP/4IIURZ10NERERUYip9tHT8+HEcPXoU+/fvR506daCtra20fufOnWVSHBEREVFxVAoylStXRs+ePcu6FiIiIqJSUSnIBAUFlXUdRERERKWm0hwZAMjJycGRI0ewevVqPH/+HACQlJSE9PT0MiuOiIiIqDgqjcgkJCTAy8sLiYmJyMrKQqdOnWBkZISFCxciKysLgYGBZV0nERERUQEqjciMHz8eTZs2xdOnT6Gnpyct79mzJ8LCwsqsOCIiIqLiqDQiExkZiZMnT0JHR0dpuYODA+7du1cmhRERERG9jUojMnl5ecjNzS2w/O7duzAyMnrnooiIiIhKQqUg07lzZyxbtky6r1AokJ6ejpkzZ/KyBURERPTeqPTR0uLFi+Hp6YnatWsjMzMTAwYMQGxsLKpVq4YtW7aUdY1EREREhVIpyNjY2ODy5cvYunUroqKikJ6eDn9/f/j6+ipN/iUiIiIqTyoFGQDQ0tLC559/Xpa1EBEREZWKSkFmw4YNxa4fNGiQSsUQERERlYZKQWb8+PFK97Ozs/HixQvo6OhAX1+fQYaIiIjeC5XOWnr69KnSLT09HdHR0WjTpg0n+xIREdF7o/K1lt7k5OSEBQsWFBitISIiIiovZRZkgFcTgJOSksqySyIiIqIiqTRHJjQ0VOm+EALJyclYuXIlWrduXSaFEREREb2NSkGmR48eSvcVCgXMzMzQoUMHLF68uCzqIiIiInorlYJMXl5eWddBREREVGplOkeGiIiI6H1SaURm0qRJJW67ZMkSVXZBRERE9FYqBZmLFy/i4sWLyM7OhouLCwAgJiYGmpqaaNy4sdROoVCUTZVEREREhVApyHTv3h1GRkZYv349TE1NAbz6krwhQ4agbdu2mDx5cpkWSURERFQYhRBClHaj6tWr49ChQ6hTp47S8qtXr6Jz584V6rtk0tLSYGJigtTUVBgbG5dp34rZHHGiik/MLPWfOBGR2pX0/7dKk33T0tLw8OHDAssfPnyI58+fq9IlERERUampFGR69uyJIUOGYOfOnbh79y7u3r2LP//8E/7+/ujVq1dZ10hERERUKJXmyAQGBmLKlCkYMGAAsrOzX3WkpQV/f398//33ZVogERERUVFUmiOTLyMjA7du3QIAODo6wsDAoMwKKyucI0P/dZwjQ0RyVK5zZPIlJycjOTkZTk5OMDAwwDtkIiIiIqJSUynIPH78GB07doSzszO6du2K5ORkAIC/vz9PvSYiIqL3RqUgM3HiRGhrayMxMRH6+vrS8r59++LAgQNlVhwRERFRcVQKMocOHcLChQthY2OjtNzJyQkJCQkqFbJgwQIoFApMmDBBWpaZmYkxY8agatWqMDQ0hI+PD+7fv69S/0RERPThUSnIZGRkKI3E5Hvy5Al0dXVL3d/Zs2exevVq1K9fX2n5xIkTsXv3buzYsQMRERFISkri6d1EREQkUSnItG3bFhs2bJDuKxQK5OXlYdGiRWjfvn2p+kpPT4evry9++eUX6XIHAJCamoq1a9diyZIl6NChA5o0aYKgoCCcPHkSf//9typlExER0QdGpe+RWbRoETp27Ihz587h5cuXmDp1Kq5du4YnT57gxIkTpeprzJgx6NatGzw8PPDtt99Ky8+fP4/s7Gx4eHhIy1xdXWFnZ4dTp06hZcuWhfaXlZWFrKws6X5aWlopj46IiIjkQqURmbp16yImJgZt2rSBt7c3MjIy0KtXL1y8eBGOjo4l7mfr1q24cOEC5s+fX2BdSkoKdHR0ULlyZaXlFhYWSElJKbLP+fPnw8TERLrZ2tqWuB4iIiKSl1KPyGRnZ8PLywuBgYH4+uuvVd7xnTt3MH78eBw+fBiVKlVSuZ83TZs2DZMmTZLup6WlMcwQERF9oEo9IqOtrY2oqKh33vH58+fx4MEDNG7cGFpaWtDS0kJERASWL18OLS0tWFhY4OXLl3j27JnSdvfv34elpWWR/erq6sLY2FjpRkRERB8mlT5a+vzzz7F27dp32nHHjh1x5coVXLp0Sbo1bdoUvr6+0s/a2toICwuTtomOjkZiYiLc3Nzead9ERET0YVBpsm9OTg5+++03HDlyBE2aNClwjaUlS5a8tQ8jIyPUrVtXaZmBgQGqVq0qLff398ekSZNQpUoVGBsb44svvoCbm1uRE32JiIjov6VUQeb27dtwcHDA1atX0bhxYwBATEyMUhuFouwupLh06VJoaGjAx8cHWVlZ8PT0xM8//1xm/RMREZG8lerq15qamkhOToa5uTmAV5ckWL58OSwsLMqtwHfFq1/Tfx2vfk1EclQuV79+M/Ps378fGRkZqlVIRERE9I5UmuybrxSDOURERERlrlRBRqFQFJgDU5ZzYoiIiIhKo1STfYUQGDx4sHRhyMzMTAQEBBQ4a2nnzp1lVyERERFREUoVZPz8/JTuf/7552VaDBEREVFplCrIBAUFlVcdRERERKX2TpN9iYiIiNSJQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGRLrUFm/vz5aNasGYyMjGBubo4ePXogOjpaqU1mZibGjBmDqlWrwtDQED4+Prh//76aKiYiIqKKRK1BJiIiAmPGjMHff/+Nw4cPIzs7G507d0ZGRobUZuLEidi9ezd27NiBiIgIJCUloVevXmqsmoiIiCoKhRBCqLuIfA8fPoS5uTkiIiLw8ccfIzU1FWZmZti8eTN69+4NALhx4wZq1aqFU6dOoWXLlm/tMy0tDSYmJkhNTYWxsXGZ1quYrSjT/ojKg5hZYf7EiYhKrKT/vyvUHJnU1FQAQJUqVQAA58+fR3Z2Njw8PKQ2rq6usLOzw6lTpwrtIysrC2lpaUo3IiIi+jBVmCCTl5eHCRMmoHXr1qhbty4AICUlBTo6OqhcubJSWwsLC6SkpBTaz/z582FiYiLdbG1ty7t0IiIiUpMKE2TGjBmDq1evYuvWre/Uz7Rp05Camird7ty5U0YVEhERUUWjpe4CAGDs2LHYs2cPjh07BhsbG2m5paUlXr58iWfPnimNyty/fx+WlpaF9qWrqwtdXd3yLpmIiIgqALWOyAghMHbsWAQHB+Ovv/5CjRo1lNY3adIE2traCAsLk5ZFR0cjMTERbm5u77tcIiIiqmDUOiIzZswYbN68Gbt27YKRkZE078XExAR6enowMTGBv78/Jk2ahCpVqsDY2BhffPEF3NzcSnTGEhEREX3Y1BpkVq1aBQBo166d0vKgoCAMHjwYALB06VJoaGjAx8cHWVlZ8PT0xM8///yeKyUiIqKKqEJ9j0x54PfI0H8dv0eGiORIlt8jQ0RERFQaDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkREJHvjxo2Dg4MDFAoFLl26JC2PjY1Fq1at4OzsjGbNmuHatWtF9rF27Vo4OTnB0dERw4cPR3Z29nuonN4VgwwREcle7969cfz4cdjb2ystHzlyJEaMGIGYmBh8+eWXGDx4cKHbx8XFYfr06YiMjMTNmzdx//59rFmz5j1UTu+KQYaIiGTv448/ho2NjdKyBw8e4Ny5c/j8888BAD4+Prhz5w5u3rxZYPs//vgDn376KSwtLaFQKBAQEIAtW7a8l9rp3TDIEBHRB+nOnTuwsrKClpYWAEChUMDOzg6JiYkF2iYmJiqN5jg4OBTajioeBhkiIiKSLQYZIiL6INna2iI5ORk5OTkAACEEEhMTYWdnV6CtnZ0dEhISpPvx8fGFtqOKh0GGiIg+SObm5mjcuDE2btwIAPjzzz9hY2ODmjVrFmjr4+OD0NBQpKSkQAiBwMBA9OvX732XTCpgkCEiItkbOXIkbGxscPfuXXh6ekphZfXq1Vi9ejWcnZ2xYMECBAUFSdsMGzYMoaGhAICPPvoIs2fPRuvWrVGzZk2YmZlh5MiRajkWKh2FEEKou4jylJaWBhMTE6SmpsLY2LhM+1bMVpRpf0TlQcz8oP/EiegDVdL/3xyRISIiItlikCEiIiLZYpAhIiIi2dJSdwFERFQMBefiUQWn5qm2HJEhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZkkWQ+emnn+Dg4IBKlSqhRYsWOHPmjLpLIiIiogqgwgeZbdu2YdKkSZg5cyYuXLiABg0awNPTEw8ePFB3aURERKRmFT7ILFmyBMOHD8eQIUNQu3ZtBAYGQl9fH7/99pu6SyMiIiI101J3AcV5+fIlzp8/j2nTpknLNDQ04OHhgVOnThW6TVZWFrKysqT7qampAIC0tLSyLzCz7LskKmvl8twnIspXTq8x+a9dQohi21XoIPPo0SPk5ubCwsJCabmFhQVu3LhR6Dbz58/H7NmzCyy3tbUtlxqJKjqTBSbqLoGIPmQm5fsa8/z5c5gUs48KHWRUMW3aNEyaNEm6n5eXhydPnqBq1apQKBRqrIzeJi0tDba2trhz5w6MjY3VXQ4RfYD4OiMfQgg8f/4c1tbWxbar0EGmWrVq0NTUxP3795WW379/H5aWloVuo6urC11dXaVllStXLq8SqRwYGxvzBYaIyhVfZ+ShuJGYfBV6sq+Ojg6aNGmCsLAwaVleXh7CwsLg5uamxsqIiIioIqjQIzIAMGnSJPj5+aFp06Zo3rw5li1bhoyMDAwZMkTdpREREZGaVfgg07dvXzx8+BAzZsxASkoKGjZsiAMHDhSYAEzyp6uri5kzZxb4aJCIqKzwdebDoxBvO6+JiIiIqIKq0HNkiIiIiIrDIENERESyxSBDREREssUgQ2oXHh4OhUKBZ8+eFdvOwcEBy5Ytey81EREBfN2RAwYZKrHBgwdDoVBAoVBAR0cHNWvWxJw5c5CTk/NO/bZq1QrJycnSFx+tW7eu0C8xPHv2LEaMGPFO+yKiiiP/NWXBggVKy0NCQt77N7HzdUe+GGSoVLy8vJCcnIzY2FhMnjwZs2bNwvfff/9Ofero6MDS0vKtL1xmZmbQ19d/p30RUcVSqVIlLFy4EE+fPlV3KYXi607FxyBDpaKrqwtLS0vY29tj1KhR8PDwQGhoKJ4+fYpBgwbB1NQU+vr66NKlC2JjY6XtEhIS0L17d5iamsLAwAB16tTBvn37ACh/tBQeHo4hQ4YgNTVVGv2ZNWsWAOUh3gEDBqBv375KtWVnZ6NatWrYsGEDgFffAj1//nzUqFEDenp6aNCgAf7444/yf5CIqMQ8PDxgaWmJ+fPnF9nm+PHjaNu2LfT09GBra4tx48YhIyNDWp+cnIxu3bpBT08PNWrUwObNmwt8JLRkyRLUq1cPBgYGsLW1xejRo5Geng4AfN2ROQYZeid6enp4+fIlBg8ejHPnziE0NBSnTp2CEAJdu3ZFdnY2AGDMmDHIysrCsWPHcOXKFSxcuBCGhoYF+mvVqhWWLVsGY2NjJCcnIzk5GVOmTCnQztfXF7t375ZeiADg4MGDePHiBXr27Ang1ZXQN2zYgMDAQFy7dg0TJ07E559/joiIiHJ6NIiotDQ1NTFv3jysWLECd+/eLbD+1q1b8PLygo+PD6KiorBt2zYcP34cY8eOldoMGjQISUlJCA8Px59//ok1a9bgwYMHSv1oaGhg+fLluHbtGtavX4+//voLU6dOBcDXHdkTRCXk5+cnvL29hRBC5OXlicOHDwtdXV3Ro0cPAUCcOHFCavvo0SOhp6cntm/fLoQQol69emLWrFmF9nv06FEBQDx9+lQIIURQUJAwMTEp0M7e3l4sXbpUCCFEdna2qFatmtiwYYO0vn///qJv375CCCEyMzOFvr6+OHnypFIf/v7+on///qocPhGVsddfU1q2bCmGDh0qhBAiODhY5P978vf3FyNGjFDaLjIyUmhoaIh///1XXL9+XQAQZ8+eldbHxsYKANLrRWF27NghqlatKt3n6458VfhLFFDFsmfPHhgaGiI7Oxt5eXkYMGAAevXqhT179qBFixZSu6pVq8LFxQXXr18HAIwbNw6jRo3CoUOH4OHhAR8fH9SvX1/lOrS0tPDZZ59h06ZNGDhwIDIyMrBr1y5s3boVAHDz5k28ePECnTp1Utru5cuXaNSokcr7JaLysXDhQnTo0KHASMjly5cRFRWFTZs2ScuEEMjLy0NcXBxiYmKgpaWFxo0bS+tr1qwJU1NTpX6OHDmC+fPn48aNG0hLS0NOTg4yMzPx4sWLEs+B4etOxcQgQ6XSvn17rFq1Cjo6OrC2toaWlhZCQ0Pfut2wYcPg6emJvXv34tChQ5g/fz4WL16ML774QuVafH194e7ujgcPHuDw4cPQ09ODl5cXAEhDv3v37kX16tWVtuM1Vogqno8//hienp6YNm0aBg8eLC1PT0/HyJEjMW7cuALb2NnZISYm5q19x8fH45NPPsGoUaPw3XffoUqVKjh+/Dj8/f3x8uXLUk3m5etOxcMgQ6ViYGCAmjVrKi2rVasWcnJycPr0abRq1QoA8PjxY0RHR6N27dpSO1tbWwQEBCAgIADTpk3DL7/8UmiQ0dHRQW5u7ltradWqFWxtbbFt2zbs378fffr0gba2NgCgdu3a0NXVRWJiItzd3d/lkInoPVmwYAEaNmwIFxcXaVnjxo3xzz//FHjdyefi4oKcnBxcvHgRTZo0AfBqZOT1s6DOnz+PvLw8LF68GBoar6aGbt++Xakfvu7IF4MMvTMnJyd4e3tj+PDhWL16NYyMjPDVV1+hevXq8Pb2BgBMmDABXbp0gbOzM54+fYqjR4+iVq1ahfbn4OCA9PR0hIWFoUGDBtDX1y/yHdOAAQMQGBiImJgYHD16VFpuZGSEKVOmYOLEicjLy0ObNm2QmpqKEydOwNjYGH5+fmX/QBDRO6lXrx58fX2xfPlyadmXX36Jli1bYuzYsRg2bBgMDAzwzz//4PDhw1i5ciVcXV3h4eGBESNGYNWqVdDW1sbkyZOhp6cnfaVDzZo1kZ2djRUrVqB79+44ceIEAgMDlfbN1x0ZU/ckHZKP1yfmvenJkydi4MCBwsTEROjp6QlPT08RExMjrR87dqxwdHQUurq6wszMTAwcOFA8evRICFFwsq8QQgQEBIiqVasKAGLmzJlCCOVJd/n++ecfAUDY29uLvLw8pXV5eXli2bJlwsXFRWhrawszMzPh6ekpIiIi3vmxIKJ3V9hrSlxcnNDR0RGv/3s6c+aM6NSpkzA0NBQGBgaifv364rvvvpPWJyUliS5dughdXV1hb28vNm/eLMzNzUVgYKDUZsmSJcLKykp6fdqwYQNfdz4QCiGEUGOOIiIiKlN3796Fra0tjhw5go4dO6q7HCpnDDJERCRrf/31F9LT01GvXj0kJydj6tSpuHfvHmJiYqT5K/Th4hwZIiKStezsbPzf//0fbt++DSMjI7Rq1QqbNm1iiPmP4IgMERERyRYvUUBERESyxSBDREREssUgQ0RERLLFIENERESyxSBDREREssUgQ0SyEh4eDoVCgWfPnqm7FCKqABhkiEglDx8+xKhRo2BnZwddXV1YWlrC09MTJ06cKLN9tGvXDhMmTFBa1qpVKyQnJ8PExKTM9qOqwYMHo0ePHuoug+g/jV+IR0Qq8fHxwcuXL7F+/Xp89NFHuH//PsLCwvD48eNy3a+Ojg4sLS3LdR9EJCPqvNATEcnT06dPBQARHh5ebBt/f39RrVo1YWRkJNq3by8uXbokrZ85c6Zo0KCB2LBhg7C3txfGxsaib9++Ii0tTQjx6oKCAJRucXFxBS4yGhQUJExMTMTu3buFs7Oz0NPTEz4+PiIjI0OsW7dO2Nvbi8qVK4svvvhC5OTkSPvPzMwUkydPFtbW1kJfX180b95cHD16VFqf3++BAweEq6urMDAwEJ6eniIpKUmq/836Xt+eiN4PfrRERKVmaGgIQ0NDhISEICsrq9A2ffr0wYMHD7B//36cP38ejRs3RseOHfHkyROpza1btxASEoI9e/Zgz549iIiIwIIFCwAAP/74I9zc3DB8+HAkJycjOTkZtra2he7rxYsXWL58ObZu3YoDBw4gPDwcPXv2xL59+7Bv3z78/vvvWL16Nf744w9pm7Fjx+LUqVPYunUroqKi0KdPH3h5eSE2Nlap3x9++AG///47jh07hsTEREyZMgUAMGXKFHz22Wfw8vKS6mvVqtU7P7ZEVErqTlJEJE9//PGHMDU1FZUqVRKtWrUS06ZNE5cvXxZCCBEZGSmMjY1FZmam0jaOjo5i9erVQohXIxr6+vrSCIwQQvzvf/8TLVq0kO67u7uL8ePHK/VR2IgMAHHz5k2pzciRI4W+vr54/vy5tMzT01OMHDlSCCFEQkKC0NTUFPfu3VPqu2PHjmLatGlF9vvTTz8JCwsL6b6fn5/w9vYu0eNFROWDc2SISCU+Pj7o1q0bIiMj8ffff2P//v1YtGgRfv31V2RkZCA9PR1Vq1ZV2ubff//FrVu3pPsODg4wMjKS7ltZWeHBgwelrkVfXx+Ojo7SfQsLCzg4OMDQ0FBpWX7fV65cQW5uLpydnZX6ycrKUqr5zX5VrY+Iyg+DDBGprFKlSujUqRM6deqE6dOnY9iwYZg5cyZGjx4NKysrhIeHF9imcuXK0s9vXp1YoVAgLy+v1HUU1k9xfaenp0NTUxPnz5+HpqamUrvXw09hfQheZ5eoQmGQIaIyU7t2bYSEhKBx48ZISUmBlpYWHBwcVO5PR0cHubm5ZVfg/69Ro0bIzc3FgwcP0LZtW5X7Ka/6iKjkONmXiErt8ePH6NChAzZu3IioqCjExcVhx44dWLRoEby9veHh4QE3Nzf06NEDhw4dQnx8PE6ePImvv/4a586dK/F+HBwccPr0acTHx+PRo0cqjdYUxtnZGb6+vhg0aBB27tyJuLg4nDlzBvPnz8fevXtLVV9UVBSio6Px6NEjZGdnl0l9RFRyDDJEVGqGhoZo0aIFli5dio8//hh169bF9OnTMXz4cKxcuRIKhQL79u3Dxx9/jCFDhsDZ2Rn9+vVDQkICLCwsSryfKVOmQFNTE7Vr14aZmRkSExPL7BiCgoIwaNAgTJ48GS4uLujRowfOnj0LOzu7EvcxfPhwuLi4oGnTpjAzMyvTLwMkopJRCH7gS0RERDLFERkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIikq3/D3rfwHeSOtKdAAAAAElFTkSuQmCC", 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pU6fU2xk+fLiSk5O1aNEiffHFF+rSpYvi4+N14sQJGWOUkJCgb775RqtWrdKuXbsUERGh+Ph4nT9/XtKPrxbr0qWLHA6HUlJStGXLFhUUFKhnz54qLi4uz7d2zVasWKF+/fopNjZWW7duVU5OjrZu3arY2Fj169eP6AEAwA3KdA5P7dq15XA45HA41LhxY5foKSoqUm5urh5//PFSbevChQt65513tGrVKrVv316SNGXKFK1Zs0bz5s3TkCFD9Nlnn2nfvn1q1qyZJGnevHkKDg7W0qVLNXz4cG3ZskVHjhzRrl27FBAQIEl68803Vbt2baWkpCg+Pr4s3941Kyoq0vjx43X//fdr5cqV8vD4sSd//etfa+XKlUpISNDTTz+t3r17y9PT87rOBgDAzaxMwTNnzhwZY/Too49q6tSpqlWrlnNZ9erVFRkZqdatW5dqW4WFhSoqKpKPj4/L9b6+vtq8ebMefPBBSXJZ7uHhIW9vb23evFnDhw9Xfn6+HA6HvL29nev4+PjIw8NDmzdvvmLw5OfnKz8/33k5Ozu7VDP/kk8//VRHjhzR0qVLnbFz6ewTJ05UmzZt9Omnn6pjx44Vcp/X4sKFC5KktLQ0N08ClN1P/25/+neMmws/v24cVeb/qimHDRs2mIKCgvLc1EXr1q1Nhw4dzIkTJ0xhYaFZtGiR8fDwMI0bNzYFBQUmPDzc9O/f32RlZZn8/Hwzc+ZMI8l06dLFGGNMZmamCQgIMGPGjDHnz583ubm5ZtSoUUaSGTFixBXv98UXXzSSSnydO3fumr6ft956y0gyOTk5l12enZ1tJJm33nrrmu6noixevPiyjwNffN1IX4sXL3b3f6Wq48QuY14M+PFPy/Hz68b7qoz/q+fOnTNS6X5/l+tl6R06dHD+/eLFiyU+Lf2np5d+yaJFi/Too4/q1ltvlaenp+Li4jRw4EClpqaqWrVqWrFihX7729+qTp068vT0VHx8vLp37y5jjCSpXr16evvtt/XEE09o7ty58vDw0MCBAxUXF1fiCMulJk6cqKeeesp5OTs7W2FhYWV5CC7rpzdd3Ldvn37961+XWL5v3z6X9dwtMjJSkrR48WI1bdrUvcMAZZSWlqbBgwc7/x3j5sLPrxtHVfm/Wq7gycvL0zPPPKNly5bp+++/L7G8tC/BbtiwoTZu3Kjz588rOztbISEhevDBB3XbbbdJ+vE9fnbv3q1z586poKBA9erVU6tWrdSyZUvnNrp06aJDhw7pu+++k5eXlwIDAxUcHOzcxuV4e3u7PA1WUdq1a6fIyEj9/ve/dzmHR5KKi4s1Y8YMRUVFqV27dhV+3+Xh6+srSWratKni4uLcPA1QPj/9O8bNhZ9fNx53/18t16u0JkyYoJSUFM2bN0/e3t7629/+pqlTpyo0NFQLFy4s8/Zq1KihkJAQnTlzRh9++KF69+7tsrxWrVqqV6+e0tPTtWPHjhLLJalu3boKDAxUSkqKMjMz1atXr/J8a9fE09NTs2bN0tq1a5WQkODyKq2EhAStXbtWr776KicsAwBwnZXrCM+aNWu0cOFCdezYUYmJiWrXrp2io6MVERGhJUuWaNCgQaXazocffihjjJo0aaKvv/5aEyZMUExMjBITEyVJb7/9turVq6fw8HB98cUXGjNmjBISEtSlSxfnNpKSktS0aVPVq1dPW7du1ZgxYzRu3Dg1adKkPN/aNevbt6+WL1+u8ePHq02bNs7ro6KitHz5cvXt29ctcwEAcDMrV/BkZWU5nzIKCAhQVlaWJKlt27Z64oknSr2dc+fOaeLEifr2229Vp04dPfDAA5o+fbqqVasmScrIyNBTTz2l06dPKyQkREOGDNHzzz/vso0DBw5o4sSJysrKUmRkpCZPnqxx48aV59uqMH379lXv3r316aefKiMjQyEhIWrXrh1HdgAAcJNyBc9tt92mw4cPKzw8XDExMVq2bJn+67/+S2vWrFFgYGCptzNgwAANGDDgistHjx6t0aNHX3UbM2fO1MyZM0t9n9eLp6dnlXjpOQAAKOc5PImJidqzZ48k6bnnntOf//xn+fj4aNy4cZowYUKFDggAAHCtynWE59KnjOLj47V//36lpqYqOjpazZs3r7DhAAAAKkKZg6e4uFgLFizQihUrdOTIETkcDkVFRTk/PwoAAKCqKdNTWsYY9erVS8OHD9eJEycUGxurZs2a6ejRoxo2bJj69OlTWXMCAACUW5mO8CxYsECbNm3S+vXr1alTJ5dlKSkpSkhI0MKFCzVkyJAKHRIAAOBalOkIz9KlSzVp0qQSsSNJ9957r5577jktWbKkwoYDAACoCGUKnr1796pbt25XXN69e3fnq7cAAACqijIFT1ZWloKCgq64PCgoSGfOnLnmoQAAACpSmYKnqKhIXl5XPu3H09NThYWF1zwUro+YmBilpqYqJibG3aMAACxVVX7XlOmkZWOMhg0bdsVPGs/Pz6+QoXB9+Pn58SnDAIBKVVV+15QpeIYOHfqL6/AKLQAAUNWUKXiSkpIqaw4AAIBKU67P0gIAALiREDwAAMB6BA8AALAewQMAAKxH8AAAAOsRPAAAwHoEDwAAsB7BAwAArFemNx4EgKoiLy9PkrRz5043T1J1+J49qKaS0vbv14VTxe4ep1KlpaW5ewTcYAgeADek/fv3S5Iee+wxN09SdbQI9tDO39XUoEGDtMvy4PmJv7+/u0fADYLgAXBDSkhIkPTjJzH7+fm5d5gqwlF4UWm5x/T3+8JlvHzcPU6l8/f3V6NGjdw9Bm4QDmOMcfcQ7padna1atWrp3LlzCggIcPc4AACgFMry+5uTlgEAgPUIHgAAYD2CBwAAWI/gAQAA1iN4AACA9QgeAABgPYIHAABYj+ABAADWI3gAAID1CB4AAGA9ggcAAFiP4AEAANYjeAAAgPUIHgAAYD2CBwAAWI/gAQAA1iN4AACA9QgeAABgPYIHAABYj+ABAADWI3gAAID1CB4AAGA9ggcAAFiP4AEAANYjeAAAgPUIHgAAYD2CBwAAWI/gAQAA1iN4AACA9QgeAABgPYIHAABYj+ABAADWI3gAAID1CB4AAGA9ggcAAFiP4AEAANYjeAAAgPUIHgAAYD2CBwAAWI/gAQAA1iN4AACA9QgeAABgPYIHAABYj+ABAADWI3gAAID1CB4AAGA9ggcAAFiP4AEAANYjeAAAgPUIHgAAYD2CBwAAWI/gAQAA1iN4AACA9QgeAABgPYIHAABYj+ABAADWI3gAAID1CB4AAGA9ggcAAFiP4AEAANYjeAAAgPUIHgAAYD2CBwAAWI/gAQAA1iN4AACA9QgeAABgPYIHAABYj+ABAADWI3gAAID1CB4AAGA9ggcAAFiP4AEAANYjeAAAgPUIHgAAYD2CBwAAWI/gAQAA1iN4AACA9QgeAABgPYIHAABYj+ABAADWI3gAAID1CB4AAGA9ggcAAFiP4AEAANYjeAAAgPUIHgAAYD2CBwAAWI/gAQAA1iN4AACA9QgeAABgPYIHAABYj+ABAADWI3gAAID1CB4AAGA9ggcAAFiP4AEAANYjeAAAgPUIHgAAYD2CBwAAWI/gAQAA1iN4AACA9QgeAABgPYIHAABYj+ABAADWI3gAAID1CB4AAGA9ggcAAFiP4AEAANYjeAAAgPUIHgAAYD2CBwAAWI/gAQAA1iN4AACA9QgeAABgPYIHAABYj+ABAADWI3gAAID1CB4AAGA9ggcAAFiP4AEAANYjeAAAgPUIHgAAYD2CBwAAWI/gAQAA1iN4AACA9QgeAABgPYIHAABYj+ABAADWI3gAAID1CB4AAGA9ggcAAFiP4AEAANYjeAAAgPUIHgAAYD2CBwAAWI/gAQAA1iN4AACA9QgeAABgPYIHAABYj+ABAADWI3gAAID1CB4AAGA9ggcAAFiP4AEAANYjeAAAgPUIHgAAYD2CBwAAWI/gAQAA1iN4AACA9QgeAABgPYIHAABYj+ABAADWI3gAAID1vNw9QFVgjJEkZWdnu3kSAABQWj/93v7p9/jVEDyScnJyJElhYWFungQAAJRVTk6OatWqddV1HKY0WWS54uJinTx5Uv7+/nI4HO4ex2rZ2dkKCwvT8ePHFRAQ4O5xblrsh6qB/VA1sB+qhvLsB2OMcnJyFBoaKg+Pq5+lwxEeSR4eHmrQoIG7x7ipBAQE8IOlCmA/VA3sh6qB/VA1lHU//NKRnZ9w0jIAALAewQMAAKxH8OC68vb21osvvihvb293j3JTYz9UDeyHqoH9UDVU9n7gpGUAAGA9jvAAAADrETwAAMB6BA8AALAewQMAAKxH8KDCzZgxQ3fffbf8/f1Vv359JSQk6MCBAy7rXLx4USNHjtQtt9yimjVr6oEHHtDp06fdNPHNYebMmXI4HBo7dqzzOvbD9XHixAkNHjxYt9xyi3x9fRUbG6sdO3Y4lxtj9MILLygkJES+vr6Kj49Xenq6Gye2T1FRkZ5//nlFRUXJ19dXDRs21EsvveTyGUzsh8qxadMm9ezZU6GhoXI4HFq5cqXL8tI87llZWRo0aJACAgIUGBio3/72t8rNzS3THAQPKtzGjRs1cuRIffbZZ0pOTtYPP/ygLl266Pz58851xo0bpzVr1ujtt9/Wxo0bdfLkSfXt29eNU9tt+/btev3119W8eXOX69kPle/MmTO65557VK1aNa1bt05fffWVZs2apdq1azvXeeWVVzR37lzNnz9f27ZtU40aNdS1a1ddvHjRjZPb5eWXX9a8efP0pz/9SWlpaXr55Zf1yiuv6I9//KNzHfZD5Th//rzuuOMO/fnPf77s8tI87oMGDdKXX36p5ORkrV27Vps2bdKIESPKNogBKllmZqaRZDZu3GiMMebs2bOmWrVq5u2333auk5aWZiSZrVu3umtMa+Xk5JhGjRqZ5ORk06FDBzNmzBhjDPvhenn22WdN27Ztr7i8uLjYBAcHmz/84Q/O686ePWu8vb3N0qVLr8eIN4UePXqYRx991OW6vn37mkGDBhlj2A/XiyTz7rvvOi+X5nH/6quvjCSzfft25zrr1q0zDofDnDhxotT3zREeVLpz585JkurUqSNJSk1N1Q8//KD4+HjnOjExMQoPD9fWrVvdMqPNRo4cqR49erg83hL74XpZvXq1WrZsqf79+6t+/fpq0aKF3njjDefyw4cP69SpUy77oVatWmrVqhX7oQK1adNG69ev18GDByVJe/bs0ebNm9W9e3dJ7Ad3Kc3jvnXrVgUGBqply5bOdeLj4+Xh4aFt27aV+r748FBUquLiYo0dO1b33HOPbr/9dknSqVOnVL16dQUGBrqsGxQUpFOnTrlhSnv985//1M6dO7V9+/YSy9gP18c333yjefPm6amnntKkSZO0fft2jR49WtWrV9fQoUOdj3VQUJDL7dgPFeu5555Tdna2YmJi5OnpqaKiIk2fPl2DBg2SJPaDm5TmcT916pTq16/vstzLy0t16tQp074heFCpRo4cqX379mnz5s3uHuWmc/z4cY0ZM0bJycny8fFx9zg3reLiYrVs2VK///3vJUktWrTQvn37NH/+fA0dOtTN0908li1bpiVLluitt95Ss2bNtHv3bo0dO1ahoaHsh5sET2mh0owaNUpr167VJ598ogYNGjivDw4OVkFBgc6ePeuy/unTpxUcHHydp7RXamqqMjMzFRcXJy8vL3l5eWnjxo2aO3euvLy8FBQUxH64DkJCQvSrX/3K5bqmTZvq2LFjkuR8rH/+6jj2Q8WaMGGCnnvuOT300EOKjY3VI488onHjxmnGjBmS2A/uUprHPTg4WJmZmS7LCwsLlZWVVaZ9Q/CgwhljNGrUKL377rtKSUlRVFSUy/K77rpL1apV0/r1653XHThwQMeOHVPr1q2v97jW6ty5s7744gvt3r3b+dWyZUsNGjTI+Xf2Q+W75557Srwtw8GDBxURESFJioqKUnBwsMt+yM7O1rZt29gPFSgvL08eHq6/8jw9PVVcXCyJ/eAupXncW7durbNnzyo1NdW5TkpKioqLi9WqVavS39k1n3IN/MwTTzxhatWqZTZs2GAyMjKcX3l5ec51Hn/8cRMeHm5SUlLMjh07TOvWrU3r1q3dOPXN4dJXaRnDfrgePv/8c+Pl5WWmT59u0tPTzZIlS4yfn59ZvHixc52ZM2eawMBAs2rVKrN3717Tu3dvExUVZS5cuODGye0ydOhQc+utt5q1a9eaw4cPmxUrVpi6deuaZ555xrkO+6Fy5OTkmF27dpldu3YZSWb27Nlm165d5ujRo8aY0j3u3bp1My1atDDbtm0zmzdvNo0aNTIDBw4s0xwEDyqcpMt+JSUlOde5cOGCefLJJ03t2rWNn5+f6dOnj8nIyHDf0DeJnwcP++H6WLNmjbn99tuNt7e3iYmJMX/9619dlhcXF5vnn3/eBAUFGW9vb9O5c2dz4MABN01rp+zsbDNmzBgTHh5ufHx8zG233WYmT55s8vPzneuwHyrHJ598ctnfCUOHDjXGlO5x//77783AgQNNzZo1TUBAgElMTDQ5OTllmsNhzCVvMwkAAGAhzuEBAADWI3gAAID1CB4AAGA9ggcAAFiP4AEAANYjeAAAgPUIHgAAYD2CBwAAWI/gAVBhhg0bJofDIYfDoerVqys6OlrTpk1TYWGhu0e7KofDoZUrV7p7DACVyMvdAwCwS7du3ZSUlKT8/Hy9//77GjlypKpVq6aJEyeWaTtFRUVyOBwlPvARAMqDnyQAKpS3t7eCg4MVERGhJ554QvHx8Vq9erXy8/P19NNP69Zbb1WNGjXUqlUrbdiwwXm7BQsWKDAwUKtXr9avfvUreXt769ixY8rPz9ezzz6rsLAweXt7Kzo6Wn//+9+dt9u3b5+6d++umjVrKigoSI888oi+++475/KOHTtq9OjReuaZZ1SnTh0FBwdrypQpzuWRkZGSpD59+sjhcDgvHzp0SL1791ZQUJBq1qypu+++Wx9//LHL95qRkaEePXrI19dXUVFReuuttxQZGak5c+Y41zl79qyGDx+uevXqKSAgQPfee6/27NlTYY83gNIheABUKl9fXxUUFGjUqFHaunWr/vnPf2rv3r3q37+/unXrpvT0dOe6eXl5evnll/W3v/1NX375perXr68hQ4Zo6dKlmjt3rtLS0vT666+rZs2akn6MiXvvvVctWrTQjh079MEHH+j06dMaMGCAywxvvvmmatSooW3btumVV17RtGnTlJycLEnavn27JCkpKUkZGRnOy7m5ubrvvvu0fv167dq1S926dVPPnj117Ngx53aHDBmikydPasOGDXrnnXf017/+VZmZmS733b9/f2VmZmrdunVKTU1VXFycOnfurKysrIp/sAFcWcV8FioAGDN06FDTu3dvY8yPn4CcnJxsvL29zbBhw4ynp6c5ceKEy/qdO3c2EydONMYYk5SUZCSZ3bt3O5cfOHDASDLJycmXvb+XXnrJdOnSxeW648ePG0nOT1vu0KGDadu2rcs6d999t3n22WedlyWZd9999xe/v2bNmpk//vGPxhhj0tLSjCSzfft25/L09HQjybz22mvGGGM+/fRTExAQYC5evOiynYYNG5rXX3/9F+8PQMXhHB4AFWrt2rWqWbOmfvjhBxUXF+vhhx9Wv379tGDBAjVu3Nhl3fz8fN1yyy3Oy9WrV1fz5s2dl3fv3i1PT0916NDhsve1Z88effLJJ84jPpc6dOiQ8/4u3aYkhYSElDgS83O5ubmaMmWK3nvvPWVkZKiwsFAXLlxwHuE5cOCAvLy8FBcX57xNdHS0ateu7TJfbm6uy/coSRcuXNChQ4euev8AKhbBA6BCderUSfPmzVP16tUVGhoqLy8v/etf/5Knp6dSU1Pl6enpsv6lseLr6yuHw+Fy+Wpyc3PVs2dPvfzyyyWWhYSEOP9erVo1l2UOh0PFxcVX3fbTTz+t5ORkvfrqq4qOjpavr6/69eungoKCq97u5/OFhIS4nKv0k8DAwFJvB8C1I3gAVKgaNWooOjra5boWLVqoqKhImZmZateuXam3FRsbq+LiYm3cuFHx8fEllsfFxemdd95RZGSkvLzK/+OsWrVqKioqcrluy5YtGjZsmPr06SPpx3g5cuSIc3mTJk1UWFioXbt26a677pIkff311zpz5ozLfKdOnZKXl5fzZGgA7sFJywAqXePGjTVo0CANGTJEK1as0OHDh/X5559rxowZeu+99654u8jISA0dOlSPPvqoVq5cqcOHD2vDhg1atmyZJGnkyJHKysrSwIEDtX37dh06dEgffvihEhMTSwTM1URGRmr9+vU6deqUM1gaNWqkFStWaPfu3dqzZ48efvhhl6NCMTExio+P14gRI/T5559r165dGjFihMtRqvj4eLVu3VoJCQn66KOPdOTIEf373//W5MmTtWPHjvI8lADKieABcF0kJSVpyJAhGj9+vJo0aaKEhARt375d4eHhV73dvHnz1K9fPz355JOKiYnRY489pvPnz0uSQkNDtWXLFhUVFalLly6KjY3V2LFjFRgYWKb375k1a5aSk5MVFhamFi1aSJJmz56t2rVrq02bNurZs6e6du3qcr6OJC1cuFBBQUFq3769+vTpo8cee0z+/v7y8fGR9ONTZ++//77at2+vxMRENW7cWA899JCOHj2qoKCgsjx8AK6Rwxhj3D0EANjg22+/VVhYmD7++GN17tzZ3eMAuATBAwDllJKSotzcXMXGxiojI0PPPPOMTpw4oYMHD5Y4URqAe3HSMgCU0w8//KBJkybpm2++kb+/v9q0aaMlS5YQO0AVxBEeAABgPU5aBgAA1iN4AACA9QgeAABgPYIHAABYj+ABAADWI3gAAID1CB4AAGA9ggcAAFiP4AEAANb7fwTUc4BMVHSjAAAAAElFTkSuQmCC", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -446,7 +415,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -456,7 +425,7 @@ }, { "data": { - "image/png": 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A6+coJxRpa2vD3t4eU6ZMQUpKilq7jRs34oMPPoC+vj6MjIzQrl07HDx4MFd/69atQ6NGjWBoaAhTU1M0adIECxYsUNtfzt+Evb29tO+8bkOGDAHwOlzs378f8fHx0NHRyffHLYcPHw5XV9dcx/XmzcXFpVSPV1ZWFhYuXAgXFxeoVCpUq1YNzZs3x//+979S9VtRMjIyMGfOHDg6OkJPTw+NGjXCkSNH1NokJydj8uTJsLOzg0qlQsuWLXHhwoUC+x0yZEiej3f9+vXV2j18+BCff/45zMzMoFKp0LBhQ1y8eFGq7auvvkLDhg1hYGAAGxsbDB48GDExMWp95PV3s3DhQml9ZGQk2rZtCwMDA7Rt2zZXMP3Pf/6DPXv2FPeh0zgGmQp07949NGvWDLdv38b27dtx584d+Pr6IjAwEG5ubnj27Fm57v/Vq1fl2n9hVCoVqlevrtEa3jX5PacZGRlQKpWwsrLS+MhDUZ93MzMzmJmZlXpfVlZWUCqVpeonR/369REbG4vIyEgsWrQIa9euhbe3t7R+2rRpGD16NPr374/r16/j/PnzaN26NXr16oVVq1ZJ7X799VdMnjwZEydOxNWrVxEaGorp06fnCkU5Lly4gNjYWMTGxkovLGFhYdKyFStWqLW3tLREjx498Ouvv+bqKzU1Fb/99huGDx+e67jevJ06dapUj9V3332HZcuWYe7cufj7778RFBSEUaNGSSOC5aEsz2nffPMN1qxZg59++gl///03xowZgz59+uDKlStSmxEjRiAgIACbN2/GjRs30KVLF7i7u+Phw4f59rtixQq1x/n+/fuoVq0a+vXrJ7VJSEhAq1atoKOjg8OHD+Pvv//GkiVLpF+pf/HiBS5fvoxZs2bh8uXL2Lt3L8LCwvL85fY5c+ao7W/ChAnSOm9vb9SoUQNXr16FtbU1pk2bJq3buXMntLS04OHhUarHUSMEVZhu3bqJmjVrihcvXqgtj42NFfr6+mLMmDHSMgBi3759au1MTEyEn5+fdD86Olr069dPmJiYiKpVq4qPP/5YRERESOu9vLxEr169xPfffy+sra2Fvb29+O6770T9+vVz1daoUSPxzTffFHoMPj4+AoDaLSgoSERERAgAYs+ePaJ9+/ZCpVKJ999/X5w+fVra1s/PT5iYmEj3r169Ktq3by8MDQ2FkZGRcHV1FRcuXMhzv97e3qJHjx7S/WXLlgkA4vDhw9IyR0dHsW7dOiGEEOfPnxfu7u7CzMxMGBsbi7Zt24pLly5JbbOzs4WPj4+wtbUVSqVSWFtbiwkTJuR73IXVunv3blGvXj2hVCqFnZ2d+PHHH/Pty8/PL9djmPO8JiQkiOHDhwtzc3NhZGQkOnToIK5evar2+Ddq1EisW7dO2NvbC4VCIYR4/feyevVq0bNnT6Gvry98fHxEUFCQACASEhLUHv99+/aJ2rVrC11dXdGlSxcRHR2dq/83rVu3Tri4uAhdXV1Rp04d8fPPP+d7bPkd75vP+9ty/nauXLlSrH4L0q5dOzFp0qRC91mQvB6LkSNHCisrKyGEEGfOnBEAxMqVK3NtO3XqVKGjoyM9tr169RJDhgwp9v6EELmexze9eZ7w9/cXWlpaIioqSq2Nn5+f0NPTk7bPbz+FAaB2fnlbo0aNxOzZswvsIysrSyxatEg4OjoKpVIpbG1txffffy+tv379uujQoYPQ09MT1apVEyNHjhTJycnS+rzOaUIUfi4sCmtra7Fq1Sq1ZX379hWenp5CCCFevHghqlSpIg4ePKjWxtXVVXz99ddF3s++ffuEQqEQkZGR0rKvvvpKtG7dulj1nj9/XgBQe77t7OzEsmXL8t2mbt260jnz0KFDol69ekKI1+ed2rVrq50L5IQjMhXk2bNnOHr0KMaOHQuVSqW2zsrKCp6enti5c2eRv448IyMDXbt2hZGREUJCQhAaGgpDQ0N069ZN7V1KYGAgwsLCEBAQgIMHD2LYsGG4deuW2nDolStXcP36dQwdOlSaV5HfZ+HTpk3Dp59+im7dukmJv2XLltL6r7/+GtOmTcPVq1fh7OyMgQMHIjMzM8++PD09UbNmTVy4cAGXLl3CjBkzoKOjk2fbdu3a4dSpU8jKygIAnDx5Eubm5ggODgbwelj27t27aN++PYDXQ8BeXl44deoUzp49CycnJ3Tv3h3JyckAgD179mDZsmVYs2YNbt++jf3796Nhw4b5Pt4F1Xrp0iV8+umnGDBgAG7cuIHZs2dj1qxZ2LBhQ5599e/fH97e3mrvivv37w8A6NevHx49eoTDhw/j0qVLcHV1RadOndRG6+7cuYM9e/Zg7969anOSZs+ejT59+uDGjRsYNmxYnvt+8eIF5s2bh02bNiE0NBTPnz/HgAED8j3urVu34ttvv8W8efNw69YtzJ8/H7NmzcLGjRvz3eZdplKppP9f27dvh6GhIUaPHp2rnbe3NzIyMqTRFCsrK5w9exZRUVHlVlv37t1haWmZ6+/Oz88Pffv2Lff5aVZWVjhx4gQeP36cb5uZM2di4cKFmDVrFv7++29s27YNlpaWAF6PHHXt2hVVq1bFhQsXsGvXLhw/fhzjx49X6+Ptc1pRzoWFndcAID09HXp6emrLVCqVNFKVmZmJrKysAtsUxfr16+Hu7g47Oztpmb+/P5o1a4Z+/fqhevXqaNKkCdatW1dgP4mJiVAoFLme14ULF8LMzAxNmjTBDz/8oHb+bdSoEY4fP47s7GwcO3YM77//PgDgyy+/xLhx42Bra1vk46hUNJ2k/i3Onj2b5yhLjqVLlwoAIj4+XghR+IjM5s2bRZ06dUR2dra0Pj09XahUKnH06FEhxOt3L5aWliI9PV2tn48++kh88cUX0v0JEyaI9u3bCyGEOHfunKhTp4548OBBvseS867oTTnvcP/3v/9Jy/766y8BQNy6dUsIkfuduZGRkdiwYUO++3lTQkKC0NLSEhcuXBDZ2dmiWrVqYsGCBaJ58+ZCCCG2bNkiatSoke/2WVlZwsjISBw4cEAIIcSSJUuEs7OzePXqVZH2X1Ctn332mejcubPasi+//FJ6t5OXvN4Vh4SECGNjY5GWlqa23NHRUaxZs0baTkdHRzx69EitDQAxefJktWV5jcgAEGfPnpXa3Lp1SwAQ586dy7MuR0dHsW3bNrV+586dK9zc3PI9tre9KyMyFy9eFObm5uKTTz4RQrweYS1oZMPY2Fj6fxYTEyNatGghAAhnZ2fh5eUldu7cKbKysvLdX46ijsgIIcSMGTOEg4ODdF64c+eOUCgU4vjx42r70dLSEgYGBmq30aNHF/h4oJARmb/++kvUrVtXaGlpiYYNG4rRo0eLQ4cOSeuTkpKErq6uNGr6trVr14qqVauKlJQUadkff/whtLS0RFxcnBAi73NaUc6FRTmvDRw4UNSrV0+Eh4eLrKwscezYMaFSqYRSqZTauLm5iXbt2omHDx+KzMxMsXnzZqGlpSWcnZ3z7fdNDx8+FFWqVBE7d+5UW66rqyt0dXXFzJkzxeXLl8WaNWuEnp5evuecly9fCldXV/HZZ5+pLV+yZIkICgoS165dE7/88oswNTUVU6ZMkdY/ePBA9OjRQ9ja2ooePXqIBw8eiJMnT4pmzZqJp0+fin79+gkHBwcxevToXK8blRlHZCqYKGTEpaif7V+7dg137tyBkZERDA0NYWhoiGrVqiEtLQ13796V2jVs2DBXnyNHjsT27duRlpaGV69eYdu2bdI7+A8//BD//PMPatSoUcwjey0n4QOAtbU1AODRo0d5tp06dSpGjBgBd3d3LFy4UK3ut5mamqJRo0YIDg7GjRs3oFQqMWrUKFy5cgUpKSk4efIk2rVrJ7WPj4/HyJEj4eTkBBMTExgbGyMlJQXR0dEAXo98vHz5Eu+99x5GjhyJffv25TtyVFitt27dQqtWrdTat2rVCrdv35ZGkIri2rVrSElJgZmZmfScGhoaIiIiQm1/dnZ2sLCwyLV9s2bNCt2HtrY2PvjgA+m+i4sLTE1NcevWrVxtU1NTcffuXQwfPlytnu+//77A5+pdcuPGDRgaGkKlUuHDDz+Em5ub2tyXwv4/57C2tsaZM2dw48YNTJo0CZmZmfDy8kK3bt2QnZ1dZvUOGzYMERERCAoKAvB6NMbe3h4dO3ZUa1enTh1cvXpV7TZnzpxS7btevXq4efMmzp49i2HDhuHRo0fo2bMnRowYAeD1/5P09HR06tQpz+1v3bqFRo0awcDAQFrWqlUrZGdnIywsTFr29jmtKOfCopzXVqxYAScnJ7i4uECpVGL8+PEYOnQotLT+/8vk5s2bIYRAjRo1oKuri5UrV2LgwIFqbQqyceNGmJqa5rpYIjs7G66urpg/fz6aNGmCUaNGYeTIkfD19c3VR0ZGBj799FMIIfDLL7+orZs6dSrat2+P999/H2PGjMGSJUvw008/IT09HQBQo0YNHDx4ENHR0Th48CDMzc0xduxY+Pr64vvvv4eRkRHCwsJw+/ZtrFmzpkjHVBkwyFSQ2rVrQ6FQ5PmCAbz+T2xhYSENEyoUilwnyYyMDOnfKSkpaNq0aa6TUXh4OD777DOp3ZsnhRw9e/aErq4u9u3bhwMHDiAjIwOffPJJGRwl1D4ayplkmt+Jevbs2fjrr7/Qo0cPnDhxAvXq1cO+ffvy7bt9+/YIDg6WQku1atVQt25dnDp1KleQ8fLywtWrV7FixQqcPn0aV69ehZmZmTTUbGtri7CwMKxevRoqlQpjx45F27Zt1R7j0tRaEikpKbC2ts71nIaFheHLL7+U2uX1nBa0vDT1AK+vtnmznpwXq3+DnBf8W7du4eXLl/D395c+CnF2dsa9e/fynHAaExODpKQkODs7qy1v0KABxo4diy1btiAgIAABAQE4efJkmdXr5OSENm3awM/PD9nZ2di0aROGDh2aa8K3UqlE7dq11W5lMRFfS0sLH3zwASZPnoy9e/diw4YNWL9+PSIiInJ9pF5Sb/+dF/VcWBgLCwvs378fqampiIqKwj///ANDQ0O89957UhtHR0ecPHkSKSkpuH//Ps6fP4+MjAy1NvkRQuDXX3/FoEGDcr25tLa2Rr169dSW1a1bV3rjlSMnxERFRSEgIADGxsYF7rN58+bIzMzM9yO1+fPno0uXLmjatCmCg4Ph4eEBHR0d9O3bV/rYXg4YZCqImZkZOnfujNWrV+Ply5dq6+Li4rB161bpkkrg9X+q2NhY6f7t27fx4sUL6b6rqytu376N6tWr5zohFXa5rba2Nry8vODn5wc/Pz8MGDCgWCcZpVJZrJGGgjg7O2PKlCk4duwY+vbtCz8/v3zb5syTCQwMlObCtG/fHtu3b0d4eLi0DABCQ0MxceJEdO/eHfXr14euri6ePHmi1p9KpULPnj2xcuVKBAcHS++Yi1tr3bp1ERoaqtY2NDQUzs7OqFKlSp595fUYurq6Ii4uDtra2rmeU3Nz83zrKo7MzEzpkk7g9ZUwz58/R926dXO1tbS0hI2NDe7du5erHgcHhzKpp7LLecG3t7fP9eIzYMAApKSk5PnO9ccff4SOjk6BV4DkvHClpqaWac3Dhw/Hnj17sGfPHjx8+FDtvFLR3jxGJycnqFSqfC+xr1u3Lq5du6b2eISGhkJLSwt16tTJdx+lORfmRU9PDzVq1EBmZib27NmDXr165WpjYGAAa2trJCQk4OjRo3m2edvJkydx584dtavHcrRq1Upt1AkAwsPD1ebR5ISY27dv4/jx40W6wu/q1avQ0tLKM6TeunUL27Ztw9y5cwG8vnw+541cRkZGmZ3jKwKDTAVatWoV0tPT0bVrV/z555+4f/8+jhw5gs6dO8PZ2Rnffvut1LZjx45YtWoVrly5gosXL2LMmDFqox2enp4wNzdHr169EBISgoiICAQHB2PixIl48OBBobWMGDECJ06cwJEjR9Qmhp4/fx4uLi4FXk5ob2+P69evIywsDE+ePMl3FKMgL1++xPjx4xEcHIyoqCiEhobiwoULeb6g5mjbti2Sk5Nx8OBBtSCzdetWWFtbq737dXJywubNm3Hr1i2cO3cOnp6eamEt553izZs3ce/ePWzZsgUqlUrtxFHUWr29vREYGIi5c+ciPDwcGzduxKpVq9QubXybvb09IiIicPXqVTx58gTp6elwd3eHm5sbevfujWPHjiEyMhKnT5/G119/rRY+SkNHRwcTJkzAuXPncOnSJQwZMgQtWrTAhx9+mGf77777DgsWLMDKlSsRHh6OGzduwM/PD0uXLi2Tegqyb9++XN9t4uLiojYSNnPmTAwePLjca8mLm5sbJk2ahC+//BJLlizB3bt38c8//+Cbb77BihUrsGTJEmny5BdffIG5c+ciNDQUUVFROHv2LAYPHgwLC4sy+c6bN/Xr1w86OjoYPXo0unTpkucEzszMTMTFxand4uPjS7XfTz75BMuWLcO5c+cQFRWF4OBgjBs3Ds7OznBxcYGenh6++uorTJ8+HZs2bcLdu3dx9uxZrF+/HsDrc5qenh68vLxw8+ZNBAUFYcKECRg0aJA0CpaXopwLi3JeO3fuHPbu3Yt79+4hJCRE+thv+vTpUpujR4/iyJEjiIiIQEBAADp06AAXFxcMHTpUapPf3+T69evRvHlzNGjQINe6KVOm4OzZs5g/fz7u3LmDbdu2Ye3atRg3bhwASKPmFy9exNatW5GVlSU9bzkjgmfOnMHy5ctx7do13Lt3D1u3bsWUKVPw+eefS5dx5xBCYNSoUVi2bJk0wtWqVSusW7cOt27dwqZNm3J9XF6paXB+zr9SRESENGFNoVAIAKJv374iNTVVrd3Dhw9Fly5dhIGBgXBychKHDh3Kdfl1bGysGDx4sDA3Nxe6urrivffeEyNHjhSJiYlCiLwn5b6pTZs2uS7FzplYWNCkvkePHonOnTsLQ0PDXJdfvzlhMyEhQVovhPqkz/T0dDFgwADp8mcbGxsxfvx48fLlywIfv0aNGkmXvwohxNOnT4VCoRADBgxQa3f58mXRrFkzoaenJ5ycnMSuXbvULk3ct2+faN68uTA2NhYGBgaiRYsWahMi31SUWnMuv9bR0RG1atUSP/zwQ4HHkZaWJjw8PISpqana5ddJSUliwoQJwsbGRujo6AhbW1vh6ekpXRaZ34RQ5DE5PL/Lr/fs2SPee+89oaurK9zd3dUu38yr/61bt4rGjRsLpVIpqlatKtq2bSv27t0rrW/Xrp3w8vLK91hLOtk3Z3Ly28f55v8BLy8v0a5du1x9ltfl13lZv369aNq0qdDT0xMGBgaiTZs2wt/fX63N7t27Rffu3YW1tbX0N+Th4SGuX79e6P6KM9k3x6hRowQA8dtvv+V5XHjr8n8AQldXt8DjLOy8sHbtWtGhQwdhYWEhlEqlqFWrlhgyZIjaZcZZWVni+++/F3Z2dtL/lfnz50vri3r59dsKOxcW5bwWHBws6tatK3R1dYWZmZkYNGiQePjwoVqbnTt3ivfee08olUphZWUlxo0bJ54/f67WJq+/yefPnwuVSiXWrl2b7/4PHDggGjRoIHR1dYWLi4ta25y/17xuOefXS5cuiebNmwsTExOhp6cn6tatK+bPn5/r4gEhhPD19RUeHh5qy+Lj40WnTp2EkZGR6NevX67XpMpMIUQRZ6tRufDx8cHSpUsREBCAFi1aVNh+hRBwcnLC2LFjMXXq1ArbL7177Ozs8N1335X4I4zIyEg4ODjgypUrZfZtx+3bt0fjxo2xfPnyAvfJ01/RKRQKREREwN7eXtOlEKnhR0sa9t1332HlypU4e/ZsmV69UJDHjx9j1apViIuLUxsSJSquv/76CyYmJmXy8U7Lli3VvpOoJLZu3QpDQ0OEhISUuh4ikgeOyPwLKRQKmJubY8WKFcWa1U9UHt68qkJXV7dUX8qVnJwszfUwNTXNd5I0R2SKjyMyVFlpa7oAqng8eVNlknOVVlkwMjKCkZFRoe1MTU3h4+NTJvv8t/Dx8eGv11OlxBEZIiIikq13fkQmOzsbMTExMDIy0vivABMREVHRCCGQnJwMGxubAr89+Z0PMjExMfL9ISwiIqJ/ufv376NmzZr5rn/ng0zO5+X3798v9OuciYiIqHJISkqCra1tofPe3vkgk/NxkrGxMYMMERGRzBQ2LYTfI0NERLJ35MgRNGvWDO+//z5atGiBa9euqa0/ceIEqlSpku+XJAKvf6agUaNGcHZ2RseOHQv8SQOqPBhkiIhI1hISEuDp6YmNGzfi+vXr+OGHH+Dp6SmtT0xMxIwZM9C9e/d8+8jOzoanpyeWL1+O8PBwdO/eHZMnT66A6qm0GGSIiEjW7t69CzMzM9SvXx8A0KZNG0RHR+Py5csAgPHjx+Obb74p8BejL126BG1tbXTo0AEAMHr0aBw4cABpaWnlfwBUKgwyREQka05OTnj69ClOnz4NAPD390dycjIiIyOxe/duaGlp4eOPPy6wj+joaNjZ2Un3jYyMYGxsjJiYmHKtnUrvnZ/sS0RE7zYTExPs3r0bM2fOREpKCtzc3FCvXj2kpKRg6dKlCA4O1nSJVI4YZIiISPY6dOggfSyUnp4OKysrJCQkIDY2VvpV9SdPnsDf3x+PHz/GvHnz1LavVasWoqKipPvJyclITEyEjY1NhR0DlQw/WiIiItmLjY2V/j137lx07NgRkyZNQnx8PCIjIxEZGYlPPvkE3377ba4QAwBNmzZFRkYGgoKCAABr1qxBz549oaenV2HHQCXDERkiIpK9b7/9FiEhIcjMzISbmxvWr19f6Da+vr6IiYnBnDlzoKWlhS1btmD06NFIS0uDjY0NNm/eXAGVU2m98z8amZSUBBMTEyQmJvIL8YiIiGSiqK/f/GiJiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItXLRERVWaF/PIvkcZp+JohjsgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWxpNMhkZWVh1qxZcHBwgEqlgqOjI+bOnQshhNRGCIFvv/0W1tbWUKlUcHd3x+3btzVYNREREVUWGg0yixYtwi+//IJVq1bh1q1bWLRoERYvXoyffvpJarN48WKsXLkSvr6+OHfuHAwMDNC1a1ekpaVpsHIiIiKqDBTizeGPCvaf//wHlpaWWL9+vbTMw8MDKpUKW7ZsgRACNjY28Pb2xrRp0wAAiYmJsLS0xIYNGzBgwIBC95GUlAQTExMkJibC2Ni43I6FiKhcKBSaroCoYOUUI4r6+q3REZmWLVsiMDAQ4eHhAIBr167h1KlT+OijjwAAERERiIuLg7u7u7SNiYkJmjdvjjNnzuTZZ3p6OpKSktRuRERE9G7S1uTOZ8yYgaSkJLi4uKBKlSrIysrCvHnz4OnpCQCIi4sDAFhaWqptZ2lpKa1724IFC/Ddd9+Vb+FERERUKWh0ROa3337D1q1bsW3bNly+fBkbN27Ejz/+iI0bN5a4z5kzZyIxMVG63b9/vwwrJiIiospEoyMyX375JWbMmCHNdWnYsCGioqKwYMECeHl5wcrKCgAQHx8Pa2trabv4+Hg0btw4zz51dXWhq6tb7rUTERGR5ml0RObFixfQ0lIvoUqVKsjOzgYAODg4wMrKCoGBgdL6pKQknDt3Dm5ubhVaKxEREVU+Gh2R6dmzJ+bNm4datWqhfv36uHLlCpYuXYphw4YBABQKBSZPnozvv/8eTk5OcHBwwKxZs2BjY4PevXtrsnQiIiKqBDQaZH766SfMmjULY8eOxaNHj2BjY4PRo0fj22+/ldpMnz4dqampGDVqFJ4/f47WrVvjyJEj0NPT02DlREREVBlo9HtkKgK/R4aIZI3fI0OV3b/5e2SIiIiISoNBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGRL40Hm4cOH+Pzzz2FmZgaVSoWGDRvi4sWL0nohBL799ltYW1tDpVLB3d0dt2/f1mDFREREVFloNMgkJCSgVatW0NHRweHDh/H3339jyZIlqFq1qtRm8eLFWLlyJXx9fXHu3DkYGBiga9euSEtL02DlREREVBkohBBCUzufMWMGQkNDERISkud6IQRsbGzg7e2NadOmAQASExNhaWmJDRs2YMCAAbm2SU9PR3p6unQ/KSkJtra2SExMhLGxcfkcCBFReVEoNF0BUcHKKUYkJSXBxMSk0NdvjY7I+Pv7o1mzZujXrx+qV6+OJk2aYN26ddL6iIgIxMXFwd3dXVpmYmKC5s2b48yZM3n2uWDBApiYmEg3W1vbcj8OIiIi0gyNBpl79+7hl19+gZOTE44ePYovvvgCEydOxMaNGwEAcXFxAABLS0u17SwtLaV1b5s5cyYSExOl2/3798v3IIiIiEhjtDW58+zsbDRr1gzz588HADRp0gQ3b96Er68vvLy8StSnrq4udHV1y7JMIiIiqqRKNCJz7969Mtm5tbU16tWrp7asbt26iI6OBgBYWVkBAOLj49XaxMfHS+uIiIjo36tEQaZ27dro0KEDtmzZUqqrh1q1aoWwsDC1ZeHh4bCzswMAODg4wMrKCoGBgdL6pKQknDt3Dm5ubiXeLxEREb0bShRkLl++jPfffx9Tp06FlZUVRo8ejfPnzxe7nylTpuDs2bOYP38+7ty5g23btmHt2rUYN24cAEChUGDy5Mn4/vvv4e/vjxs3bmDw4MGwsbFB7969S1I6ERERvUNKdfl1ZmYm/P39sWHDBhw5cgTOzs4YNmwYBg0aBAsLiyL1cfDgQcycORO3b9+Gg4MDpk6dipEjR0rrhRDw8fHB2rVr8fz5c7Ru3RqrV6+Gs7Nzkfov6uVbRESVEi+/pspOw5dfl8n3yKSnp2P16tWYOXMmXr16BaVSiU8//RSLFi2CtbV1absvFQYZIpI1Bhmq7OT8PTIXL17E2LFjYW1tjaVLl2LatGm4e/cuAgICEBMTg169epWmeyIiIqIClejy66VLl8LPzw9hYWHo3r07Nm3ahO7du0NL63UucnBwwIYNG2Bvb1+WtRIRERGpKVGQ+eWXXzBs2DAMGTIk34+OqlevjvXr15eqOCIiIqKCaPS3lioC58gQkaxxjgxVdnKcI+Pn54ddu3blWr5r1y7p5wWIiIiIyluJgsyCBQtgbm6ea3n16tWlnxsgIiIiKm8lCjLR0dFwcHDItdzOzk76eQEiIiKi8laiIFO9enVcv3491/Jr167BzMys1EURERERFUWJgszAgQMxceJEBAUFISsrC1lZWThx4gQmTZqEAQMGlHWNRERERHkq0eXXc+fORWRkJDp16gRt7dddZGdnY/DgwZwjQ0RERBWmVJdfh4eH49q1a1CpVGjYsKH0q9WVCS+/JiJZ4+XXVNlp+PLrEo3I5HB2di7yjzcSERERlbUSBZmsrCxs2LABgYGBePToEbKzs9XWnzhxokyKIyIiIipIiYLMpEmTsGHDBvTo0QMNGjSAgkOfREREpAElCjI7duzAb7/9hu7du5d1PURERERFVqLLr5VKJWrXrl3WtRAREREVS4mCjLe3N1asWIF3/PcmiYiIqJIr0UdLp06dQlBQEA4fPoz69etDR0dHbf3evXvLpDgiIiKigpQoyJiamqJPnz5lXQsRERFRsZQoyPj5+ZV1HURERETFVqI5MgCQmZmJ48ePY82aNUhOTgYAxMTEICUlpcyKIyIiIipIiUZkoqKi0K1bN0RHRyM9PR2dO3eGkZERFi1ahPT0dPj6+pZ1nURERES5lGhEZtKkSWjWrBkSEhKgUqmk5X369EFgYGCZFUdERERUkBKNyISEhOD06dNQKpVqy+3t7fHw4cMyKYyIiIioMCUakcnOzkZWVlau5Q8ePICRkVGpiyIiIiIqihIFmS5dumD58uXSfYVCgZSUFPj4+PBnC4iIiKjCKEQJvp73wYMH6Nq1K4QQuH37Npo1a4bbt2/D3Nwcf/75J6pXr14etZZIUlISTExMkJiYCGNjY02XQ0RUPPxRXqrsyulb/ov6+l2iIAO8vvx6x44duH79OlJSUuDq6gpPT0+1yb+VAYMMEckagwxVdhoOMiWa7AsA2tra+Pzzz0u6OREREVGplSjIbNq0qcD1gwcPLlExRERERMVRoo+WqlatqnY/IyMDL168gFKphL6+Pp49e1ZmBZYWP1oiIlnjR0tU2Wn4o6USXbWUkJCgdktJSUFYWBhat26N7du3l7hoIiIiouIo8W8tvc3JyQkLFy7EpEmTyqpLIiIiogKVWZABXk8AjomJKcsuiYiIiPJVosm+/v7+aveFEIiNjcWqVavQqlWrMimMiIiIqDAlCjK9e/dWu69QKGBhYYGOHTtiyZIlZVEXERERUaFKFGSys7PLug4iIiKiYivTOTJEREREFalEIzJTp04tctulS5eWZBdEREREhSpRkLly5QquXLmCjIwM1KlTBwAQHh6OKlWqwNXVVWqn4Bc5ERERUTkqUZDp2bMnjIyMsHHjRulbfhMSEjB06FC0adMG3t7eZVokERERUV5K9BMFNWrUwLFjx1C/fn215Tdv3kSXLl0q1XfJ8CcKiEjWOLJNlZ0cf6IgKSkJjx8/zrX88ePHSE5OLkmXRERERMVWoiDTp08fDB06FHv37sWDBw/w4MED7NmzB8OHD0ffvn3LukYiIiKiPJVojoyvry+mTZuGzz77DBkZGa870tbG8OHD8cMPP5RpgURERET5KdEcmRypqam4e/cuAMDR0REGBgZlVlhZ4RwZIpI1zpGhyk6Oc2RyxMbGIjY2Fk5OTjAwMEApMhERERFRsZUoyDx9+hSdOnWCs7MzunfvjtjYWADA8OHDeek1ERERVZgSBZkpU6ZAR0cH0dHR0NfXl5b3798fR44cKbPiiIiIiApSosm+x44dw9GjR1GzZk215U5OToiKiiqTwoiIiIgKU6IRmdTUVLWRmBzPnj2Drq5uqYsiIiIiKooSBZk2bdpg06ZN0n2FQoHs7GwsXrwYHTp0KLPiiIiIiApSoo+WFi9ejE6dOuHixYt49eoVpk+fjr/++gvPnj1DaGhoWddIRERElKcSjcg0aNAA4eHhaN26NXr16oXU1FT07dsXV65cgaOjY1nXSERERJSnYo/IZGRkoFu3bvD19cXXX39dHjURERERFUmxR2R0dHRw/fr1Mi9k4cKFUCgUmDx5srQsLS0N48aNg5mZGQwNDeHh4YH4+Pgy3zcRERHJU4k+Wvr888+xfv36MiviwoULWLNmDd5//3215VOmTMGBAwewa9cunDx5EjExMfxRSiIiIpKUaLJvZmYmfv31Vxw/fhxNmzbN9RtLS5cuLXJfKSkp8PT0xLp16/D9999LyxMTE7F+/Xps27YNHTt2BAD4+fmhbt26OHv2LFq0aJFnf+np6UhPT5fuJyUlFefQiIiISEaKNSJz7949ZGdn4+bNm3B1dYWRkRHCw8Nx5coV6Xb16tViFTBu3Dj06NED7u7uassvXbqEjIwMteUuLi6oVasWzpw5k29/CxYsgImJiXSztbUtVj1EREQkH8UakXFyckJsbCyCgoIAvP5JgpUrV8LS0rJEO9+xYwcuX76MCxcu5FoXFxcHpVIJU1NTteWWlpaIi4vLt8+ZM2di6tSp0v2kpCSGGSIiondUsYLM279uffjwYaSmppZox/fv38ekSZMQEBAAPT29EvWRF11dXX67MBER0b9EiSb75ng72BTHpUuX8OjRI7i6ukJbWxva2to4efIkVq5cCW1tbVhaWuLVq1d4/vy52nbx8fGwsrIqTdlERET0jijWiIxCoYBCoci1rCQ6deqEGzduqC0bOnQoXFxc8NVXX8HW1hY6OjoIDAyEh4cHACAsLAzR0dFwc3Mr0T6JiIjo3VLsj5aGDBkifXSTlpaGMWPG5Lpqae/evYX2ZWRkhAYNGqgtMzAwgJmZmbR8+PDhmDp1KqpVqwZjY2NMmDABbm5u+V6xRERERP8uxQoyXl5eavc///zzMi3mbcuWLYOWlhY8PDyQnp6Orl27YvXq1eW6TyIiIpIPhSjNRBcZSEpKgomJCRITE2FsbKzpcoiIiqeEH98TVZhyihFFff0u1WRfIiIiIk1ikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIiIi2WKQISIiItnSaJBZsGABPvjgAxgZGaF69ero3bs3wsLC1NqkpaVh3LhxMDMzg6GhITw8PBAfH6+hiomIiKgy0WiQOXnyJMaNG4ezZ88iICAAGRkZ6NKlC1JTU6U2U6ZMwYEDB7Br1y6cPHkSMTEx6Nu3rwarJiIiospCIYQQmi4ix+PHj1G9enWcPHkSbdu2RWJiIiwsLLBt2zZ88sknAIB//vkHdevWxZkzZ9CiRYtC+0xKSoKJiQkSExNhbGxc3odARFS2FApNV0BUsHKKEUV9/a5Uc2QSExMBANWqVQMAXLp0CRkZGXB3d5fauLi4oFatWjhz5kyefaSnpyMpKUntRkRERO+mShNksrOzMXnyZLRq1QoNGjQAAMTFxUGpVMLU1FStraWlJeLi4vLsZ8GCBTAxMZFutra25V06ERERaUilCTLjxo3DzZs3sWPHjlL1M3PmTCQmJkq3+/fvl1GFREREVNloa7oAABg/fjwOHjyIP//8EzVr1pSWW1lZ4dWrV3j+/LnaqEx8fDysrKzy7EtXVxe6urrlXTIRERFVAhodkRFCYPz48di3bx9OnDgBBwcHtfVNmzaFjo4OAgMDpWVhYWGIjo6Gm5tbRZdLRERElYxGR2TGjRuHbdu24ffff4eRkZE078XExAQqlQomJiYYPnw4pk6dimrVqsHY2BgTJkyAm5tbka5YIiIionebRi+/VuRzWaGfnx+GDBkC4PUX4nl7e2P79u1IT09H165dsXr16nw/WnobL78mIlnj5ddU2Wn48utK9T0y5YFBhohkjUGGKjt+jwwRERFRyTDIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyFCFS09Px/jx4+Hk5ISGDRvi888/z7Pd+vXr4eTkBEdHR4wcORIZGRkVXCkREVV2DDJU4WbMmAGFQoHw8HDcuHEDP/74Y642ERERmDVrFkJCQnDnzh3Ex8dj7dq1GqiWiIgqMwYZqlCpqalYv3495s2bB4VCAQCwsrLK1W737t34+OOPYWVlBYVCgTFjxmD79u0VXS4REVVyDDJUoe7evYtq1aph/vz5aNasGdq0aYPAwMBc7aKjo2FnZyfdt7e3R3R0dEWWSkREMsAgQxUqMzMTUVFRqFevHi5evIiVK1eif//+iI+P13RpREQkQwwyVKFq1aoFLS0teHp6AgCaNGkCBwcH3LhxI1e7qKgo6X5kZCRq1apVobUSEVHlxyBDFcrc3BydOnXC0aNHAbye1BsREYG6deuqtfPw8IC/vz/i4uIghICvry8GDBigiZKJiKgSY5ChCufr64sffvgBDRs2RO/evbFmzRrUqFEDI0aMgL+/PwDgvffew3fffYdWrVqhdu3asLCwwOjRozVcORERVTYKIYTQdBHlKSkpCSYmJkhMTISxsbGmyyEiKp7/u7qPqNIqpxhR1NdvjsgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbGlrugA5U3zHSXhU+Qmfd3o+PxH9y3FEhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZEsWQebnn3+Gvb099PT00Lx5c5w/f17TJREREVElUOmDzM6dOzF16lT4+Pjg8uXLaNSoEbp27YpHjx5pujQiIiLSsEofZJYuXYqRI0di6NChqFevHnx9faGvr49ff/1V06URERGRhmlruoCCvHr1CpcuXcLMmTOlZVpaWnB3d8eZM2fy3CY9PR3p6enS/cTERABAUlJS2ReYVvZdEpW1cvnbJyLKUU7nmJxzlxCiwHaVOsg8efIEWVlZsLS0VFtuaWmJf/75J89tFixYgO+++y7Xcltb23KpkaiyM1looukSiOhdZlK+55jk5GSYFLCPSh1kSmLmzJmYOnWqdD87OxvPnj2DmZkZFAqFBiujwiQlJcHW1hb379+HsbGxpsshoncQzzPyIYRAcnIybGxsCmxXqYOMubk5qlSpgvj4eLXl8fHxsLKyynMbXV1d6Orqqi0zNTUtrxKpHBgbG/MEQ0TliucZeShoJCZHpZ7sq1Qq0bRpUwQGBkrLsrOzERgYCDc3Nw1WRkRERJVBpR6RAYCpU6fCy8sLzZo1w4cffojly5cjNTUVQ4cO1XRpREREpGGVPsj0798fjx8/xrfffou4uDg0btwYR44cyTUBmORPV1cXPj4+uT4aJCIqKzzPvHsUorDrmoiIiIgqqUo9R4aIiIioIAwyREREJFsMMkRERCRbDDKkccHBwVAoFHj+/HmB7ezt7bF8+fIKqYmICOB5Rw4YZKjIhgwZAoVCAYVCAaVSidq1a2POnDnIzMwsVb8tW7ZEbGys9MVHGzZsyPNLDC9cuIBRo0aVal9EVHnknFMWLlyotnz//v0V/k3sPO/IF4MMFUu3bt0QGxuL27dvw9vbG7Nnz8YPP/xQqj6VSiWsrKwKPXFZWFhAX1+/VPsiospFT08PixYtQkJCgqZLyRPPO5UfgwwVi66uLqysrGBnZ4cvvvgC7u7u8Pf3R0JCAgYPHoyqVatCX18fH330EW7fvi1tFxUVhZ49e6Jq1aowMDBA/fr1cejQIQDqHy0FBwdj6NChSExMlEZ/Zs+eDUB9iPezzz5D//791WrLyMiAubk5Nm3aBOD1t0AvWLAADg4OUKlUaNSoEXbv3l3+DxIRFZm7uzusrKywYMGCfNucOnUKbdq0gUqlgq2tLSZOnIjU1FRpfWxsLHr06AGVSgUHBwds27Yt10dCS5cuRcOGDWFgYABbW1uMHTsWKSkpAMDzjswxyFCpqFQqvHr1CkOGDMHFixfh7++PM2fOQAiB7t27IyMjAwAwbtw4pKen488//8SNGzewaNEiGBoa5uqvZcuWWL58OYyNjREbG4vY2FhMmzYtVztPT08cOHBAOhEBwNGjR/HixQv06dMHwOtfQt+0aRN8fX3x119/YcqUKfj8889x8uTJcno0iKi4qlSpgvnz5+Onn37CgwcPcq2/e/cuunXrBg8PD1y/fh07d+7EqVOnMH78eKnN4MGDERMTg+DgYOzZswdr167Fo0eP1PrR0tLCypUr8ddff2Hjxo04ceIEpk+fDoDnHdkTREXk5eUlevXqJYQQIjs7WwQEBAhdXV3Ru3dvAUCEhoZKbZ88eSJUKpX47bffhBBCNGzYUMyePTvPfoOCggQAkZCQIIQQws/PT5iYmORqZ2dnJ5YtWyaEECIjI0OYm5uLTZs2SesHDhwo+vfvL4QQIi0tTejr64vTp0+r9TF8+HAxcODAkhw+EZWxN88pLVq0EMOGDRNCCLFv3z6R8/I0fPhwMWrUKLXtQkJChJaWlnj58qW4deuWACAuXLggrb99+7YAIJ0v8rJr1y5hZmYm3ed5R74q/U8UUOVy8OBBGBoaIiMjA9nZ2fjss8/Qt29fHDx4EM2bN5famZmZoU6dOrh16xYAYOLEifjiiy9w7NgxuLu7w8PDA++//36J69DW1sann36KrVu3YtCgQUhNTcXvv/+OHTt2AADu3LmDFy9eoHPnzmrbvXr1Ck2aNCnxfomofCxatAgdO3bMNRJy7do1XL9+HVu3bpWWCSGQnZ2NiIgIhIeHQ1tbG66urtL62rVro2rVqmr9HD9+HAsWLMA///yDpKQkZGZmIi0tDS9evCjyHBiedyonBhkqlg4dOuCXX36BUqmEjY0NtLW14e/vX+h2I0aMQNeuXfHHH3/g2LFjWLBgAZYsWYIJEyaUuBZPT0+0a9cOjx49QkBAAFQqFbp16wYA0tDvH3/8gRo1aqhtx99YIap82rZti65du2LmzJkYMmSItDwlJQWjR4/GxIkTc21Tq1YthIeHF9p3ZGQk/vOf/+CLL77AvHnzUK1aNZw6dQrDhw/Hq1evijWZl+edyodBhorFwMAAtWvXVltWt25dZGZm4ty5c2jZsiUA4OnTpwgLC0O9evWkdra2thgzZgzGjBmDmTNnYt26dXkGGaVSiaysrEJradmyJWxtbbFz504cPnwY/fr1g46ODgCgXr160NXVRXR0NNq1a1eaQyaiCrJw4UI0btwYderUkZa5urri77//znXeyVGnTh1kZmbiypUraNq0KYDXIyNvXgV16dIlZGdnY8mSJdDSej019LffflPrh+cd+WKQoVJzcnJCr169MHLkSKxZswZGRkaYMWMGatSogV69egEAJk+ejI8++gjOzs5ISEhAUFAQ6tatm2d/9vb2SElJQWBgIBo1agR9ff183zF99tln8PX1RXh4OIKCgqTlRkZGmDZtGqZMmYLs7Gy0bt0aiYmJCA0NhbGxMby8vMr+gSCiUmnYsCE8PT2xcuVKadlXX32FFi1aYPz48RgxYgQMDAzw999/IyAgAKtWrYKLiwvc3d0xatQo/PLLL9DR0YG3tzdUKpX0lQ61a9dGRkYGfvrpJ/Ts2ROhoaHw9fVV2zfPOzKm6Uk6JB9vTsx727Nnz8SgQYOEiYmJUKlUomvXriI8PFxaP378eOHo6Ch0dXWFhYWFGDRokHjy5IkQIvdkXyGEGDNmjDAzMxMAhI+PjxBCfdJdjr///lsAEHZ2diI7O1ttXXZ2tli+fLmoU6eO0NHRERYWFqJr167i5MmTpX4siKj08jqnRERECKVSKd58eTp//rzo3LmzMDQ0FAYGBuL9998X8+bNk9bHxMSIjz76SOjq6go7Ozuxbds2Ub16deHr6yu1Wbp0qbC2tpbOT5s2beJ55x2hEEIIDeYoIiKiMvXgwQPY2tri+PHj6NSpk6bLoXLGIENERLJ24sQJpKSkoGHDhoiNjcX06dPx8OFDhIeHS/NX6N3FOTJERCRrGRkZ+O9//4t79+7ByMgILVu2xNatWxli/iU4IkNERESyxZ8oICIiItlikCEiIiLZYpAhIiIi2WKQISIiItlikCEiIiLZYpAhIlkJDg6GQqHA8+fPNV0KEVUCDDJEVCKPHz/GF198gVq1akFXVxdWVlbo2rUrQkNDy2wf7du3x+TJk9WWtWzZErGxsTAxMSmz/ZTUkCFD0Lt3b02XQfSvxi/EI6IS8fDwwKtXr7Bx40a89957iI+PR2BgIJ4+fVqu+1UqlbCysirXfRCRjGjyh56ISJ4SEhIEABEcHFxgm+HDhwtzc3NhZGQkOnToIK5evSqt9/HxEY0aNRKbNm0SdnZ2wtjYWPTv318kJSUJIV7/oCAAtVtERESuHxn18/MTJiYm4sCBA8LZ2VmoVCrh4eEhUlNTxYYNG4SdnZ0wNTUVEyZMEJmZmdL+09LShLe3t7CxsRH6+vriww8/FEFBQdL6nH6PHDkiXFxchIGBgejatauIiYmR6n+7vje3J6KKwY+WiKjYDA0NYWhoiP379yM9PT3PNv369cOjR49w+PBhXLp0Ca6urujUqROePXsmtbl79y7279+PgwcP4uDBgzh58iQWLlwIAFixYgXc3NwwcuRIxMbGIjY2Fra2tnnu68WLF1i5ciV27NiBI0eOIDg4GH369MGhQ4dw6NAhbN68GWvWrMHu3bulbcaPH48zZ85gx44duH79Ovr164du3brh9u3bav3++OOP2Lx5M/78809ER0dj2rRpAIBp06bh008/Rbdu3aT6WrZsWerHloiKSdNJiojkaffu3aJq1apCT09PtGzZUsycOVNcu3ZNCCFESEiIMDY2FmlpaWrbODo6ijVr1gghXo9o6OvrSyMwQgjx5ZdfiubNm0v327VrJyZNmqTWR14jMgDEnTt3pDajR48W+vr6Ijk5WVrWtWtXMXr0aCGEEFFRUaJKlSri4cOHan136tRJzJw5M99+f/75Z2FpaSnd9/LyEr169SrS40VE5YNzZIioRDw8PNCjRw+EhITg7NmzOHz4MBYvXoz//e9/SE1NRUpKCszMzNS2efnyJe7evSvdt7e3h5GRkXTf2toajx49KnYt+vr6cHR0lO5bWlrC3t4ehoaGasty+r5x4waysrLg7Oys1k96erpazW/3W9L6iKj8MMgQUYnp6emhc+fO6Ny5M2bNmoURI0bAx8cHY8eOhbW1NYKDg3NtY2pqKv377V8nVigUyM7OLnYdefVTUN8pKSmoUqUKLl26hCpVqqi1ezP85NWH4O/sElUqDDJEVGbq1auH/fv3w9XVFXFxcdDW1oa9vX2J+1MqlcjKyiq7Av9PkyZNkJWVhUePHqFNmzYl7qe86iOiouNkXyIqtqdPn6Jjx47YsmULrl+/joiICOzatQuLFy9Gr1694O7uDjc3N/Tu3RvHjh1DZGQkTp8+ja+//hoXL14s8n7s7e1x7tw5REZG4smTJyUarcmLs7MzPD09MXjwYOzduxcRERE4f/48FixYgD/++KNY9V2/fh1hYWF48uQJMjIyyqQ+Iio6BhkiKjZDQ0M0b94cy5YtQ9u2bdGgQQPMmjULI0eOxKpVq6BQKHDo0CG0bdsWQ4cOhbOzMwYMGICoqChYWloWeT/Tpk1DlSpVUK9ePVhYWCA6OrrMjsHPzw+DBw+Gt7c36tSpg969e+PChQuoVatWkfsYOXIk6tSpg2bNmsHCwqJMvwyQiIpGIfiBLxEREckUR2SIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLYYZIiIiEi2GGSIiIhIthhkiIiISLb+Hyiwms2j4G6hAAAAAElFTkSuQmCC", + "image/png": 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582Z8/vnnhd8hytPMmTMRHx8Pa2trndtwdXVFfHw8Jk6c+Mr9yQ7TNWrUQGZmptYyGxsbrFmzRnn/bFh79jV37lyt9X755Re0bNkStra2MDU1RZUqVTB48GCcPHkyx/b/+ecf2NnZoUyZMkhLSwPwNDTntp1nX9HR0VqB/oMPPkC1atVy3cfY2Fjo6+sjJCQEAPJsM/vBpLo6ffo0OnbsCAcHB5iYmMDd3R09evTA7du3X6nd1+XEiRNo3bo1bGxsYG9vj2HDhiE5OVmrTmhoKBo1agRLS0s4OTlhypQpyMjIyLfdK1euoEuXLihbtiysrKzw/vvv49atW8ry7J/B3F5Hjx5V6okI5s+fDy8vLxgbG6NcuXKYPXu2svzgwYNo3Lgx7O3tYWpqiqpVq2LRokVafVm3bh1cXV1ha2uLCRMmaC2Ljo6Gl5cXkpKSXvqzKwoMO0Xs6tWrqF+/Pi5duoQNGzbg8uXLCAwMRGhoKLy9vXH//v0i3f6TJ0+KtP38pKenF0m7IvLCA0JxsLOzy/FQRipa2b8kNBqNzm3o6+vDyckJFhYWhdavq1evIjg4+IX1ssPas68PPvhAWT5lyhT06NEDderUQUhICC5evIj169ejYsWK8Pf3z9HeL7/8gho1aqBq1apKYO/Ro4dW+97e3hg6dKhWmaurq1Y7fn5+uHDhAg4fPpxjG2vWrIGDgwPat2+vlAUFBeXYj1cZvb1z5w5atWoFOzs77N69G+fPn0dQUBBcXFyQkpKic7svUljHrLi4OPj4+KBy5cqIjIzErl278Ndff2mNZp0+fRrt27dH27ZtcfLkSfz4448ICQnBxx9/nGe7KSkpaNOmDTQaDfbu3YtDhw7hyZMn6NChA7KysgAAjRo1yvFdDBkyBB4eHqhfv77S1tixY/Hdd99h/vz5uHDhAkJCQvD2228ry83NzTFmzBgcOHAA58+fx6effopPP/0U3377LQDg7t27GDJkCObPn4/ff/8dP/zwA7Zv366sP2rUKMydOxdWVlaF8pm+MqEi1bZtWylfvrw8fvxYqzw+Pl7MzMxkxIgRShkA2bJli1Y9a2trCQoKUt7HxsZK9+7dxdraWmxtbaVjx45y7do1ZfmAAQOkU6dOMmvWLHF2dhZ3d3f57LPPpEaNGjn6Vrt2bfn0008LtB+ZmZny2WefSbly5cTIyEhq164tO3fuVJZfu3ZNAMjGjRvlnXfeEWNjYwkKCpKMjAwZP368WFtbi52dnXz00UfSv39/6dSpk1bbc+bMEXd3dzExMZFatWrJpk2blOVhYWECQHbs2CH16tUTQ0NDCQsLe+F6IiK//fabeHp6iomJiTRv3lyCgoIEgDx48CDX/XRzcxMAysvNzU1ERKZPny61a9eW4OBgcXNzEysrK+nRo4ckJSUp6zZr1kzGjh2rvP/qq6+kcuXKYmxsLA4ODtKtW7d8P+OgoCBxdXUVU1NT6dy5s8yfP1+sra2V5dnf7bPGjh0rzZo1U97v3LlTGjdurHze7777rly+fDnH93Ty5Mk8++Hm5iYzZ86Unj17ipmZmbi4uMjy5cu16ixYsEBq1qwpZmZmUr58eRk5cqQ8evRIq87BgwelWbNmYmpqKjY2NtKmTRu5f/9+rp/V9u3bxcrKSn744Yd8P6Pn+7lo0aIC13+R7O/4RdsMCwvLc3n2z+pHH30krq6ukpqaqix7/v/yi/ofEREhAGTJkiW5Ls/KyspR1rx5cwkMDJSvv/5aWrdunet6z3/22Z7f/3r16omfn1+ObXp4eMiUKVOUstyOWy8yffp0GTBgQJ7Lt2zZIgYGBpKenp5vO+fOnZN3331XLC0txcLCQpo0aaL8vOt6zBIRWblypVStWlWMjY2lSpUq8tVXX73U/n3zzTfi4OAgmZmZStmZM2cEgFy6dElERPz9/aV+/fpa64WEhIiJiYnWceVZu3fvFj09PUlMTFTKHj58KBqNRvbs2ZPrOk+ePJGyZcvKzJkzlbK///5bDAwM5MKFCy+1X126dJG+ffuKiEhkZKQ4Ojoqy95//32ZN2+eiIisX79eOnbs+FJtFzWO7BSh+/fvY/fu3Rg1ahRMTU21ljk5OaFPnz748ccfC/wgv/T0dPj6+sLS0hLh4eE4dOgQLCws0LZtW60RnNDQUFy8eBF79uzB9u3bMXjwYJw/f15rCPPkyZM4c+YMBg0apAx7RkdH57ntJUuWYMGCBZg/fz7OnDkDX19fdOzYEZcuXdKq9/HHH2Ps2LE4f/48fH19sWDBAqxZswarV6/GwYMHcf/+fWzZskVrnYCAAAQHByMwMBB//fUXxo8fj759+2L//v052p47dy7Onz+PWrVqvXC969evo2vXrujQoQNOnTqFIUOG5PtXEwDlM8r+S/XZz+zKlSvYunUrtm/fju3bt2P//v05TjlkO3bsGD788EPMnDkTFy9exK5du/DOO+/kud3IyEj4+flhzJgxOHXqFFq0aIFZs2bl29fcpKSkYMKECTh27BhCQ0Ohp6eHLl26KH/1FdSXX36J2rVr4+TJk8p3umfPHmW5np4eli5dir/++gtr167F3r17MXnyZGX5qVOn0KpVK1SvXh0RERE4ePAgOnTokOPUDgCsX78evXr1wrp169CnT5+X3ueSaNy4ccjIyMCyZct0bmPDhg2wsLDAqFGjcl3+/GjWlStXEBERgffffx/vv/8+wsPDERMTo/P2/fz88NNPP2mNpOzbtw/Xrl3D4MGDdW63IJycnJCRkYEtW7bkeXy8efMm3nnnHRgbG2Pv3r04fvw4Bg8erIz66nrMWrduHaZNm4bZs2fj/PnzmDNnDqZOnYq1a9cq6zRv3jzfOUdpaWkwMjKCnt7/fsVm/w44ePCgUsfExERrPVNTU6SmpuL48eN5tqvRaGBsbKyUmZiYQE9PT2n3eSEhIbh37x4GDRqklG3btg0VK1bE9u3b4eHhAXd3dwwZMiTfMw0nT57E4cOH0axZMwCAp6cnHj9+jJMnT+L+/fs4evQoatWqhQcPHmDq1KlYvnx5nm0Vi+JOW2p25MiRfP/qWbhwoQCQW7duiciLR3a+//57qVKlitZfdGlpaWJqaiq7d+8Wkad//Ts6OkpaWppWO+3atZORI0cq7z/44ANp3ry5iDxN6FWqVJEbN27kuS8uLi4ye/ZsrbK33npLRo0aJSL/+ytp8eLFWnWcnZ2VtC8ikp6eLuXLl1dGKFJTU8XMzEwOHz6stZ6fn5/06tVLRP731/LWrVuV5QVZz9/fX6pXr661fMqUKfmO7Ijk/j1Mnz5dzMzMtP7i+uijj6RBgwbK+2f/Yv7ll1/Eysoqz7/QnterVy9p3769VlmPHj1eemTneXfu3BEAcvbsWREp+MhO27Ztc/SlXbt2ea6zadMmsbe319qfxo0b51k/+7Navny5WFtby759+/Ksm18/S+rIzoMHDyQwMFDs7Ozk4cOHIpL7yI6RkZGYm5trvQ4cOCAiT0eFa9WqpdX+ggULtOpmty0i8t///lc6d+6svO/UqZNMnz49Rx8LOrLz4MEDMTEx0epzv379pEmTJlrrARATE5Mc+xETE5Pn5/SikZ3s/TEwMBA7Oztp27atzJs3TxISEpTl/v7+4uHhIU+ePMl1fV2PWZUqVZL169drlX3++efi7e2tvO/Xr598/PHHefb93LlzYmBgIPPmzZO0tDS5f/++dOvWTQDInDlzROR/ozTr16+XjIwMuXHjhjRt2lQA5Nh+ttu3b4uVlZWMHTtWUlJSJDk5WcaMGSMAZNiwYbmu065duxz/d4cPHy7GxsbSoEEDOXDggISFhUmdOnWkRYsWOdbPHhnT09PTGh0SEdm8ebPUrFlTKlWqpPysDR48WBYtWiT79++XOnXqSI0aNXKMuBcHjuy8BvKCkRsjI6MCtXP69GlcvnwZlpaWsLCwgIWFBezs7JCamoorV64o9d54440cbQ4dOhQbNmxAamoqnjx5gvXr1yt/nb399tu4cOECypUrl+t2k5KSEBcXh8aNG2uVN27cGOfPn9cqe/accGJiIuLj49GgQQOlzMDAQKvO5cuX8fjxY7Ru3VrZJwsLCwQHB2vt0/NtF2S98+fPa20bALy9vXPdx4Jwd3fXmpPj7Oyc52TJ1q1bw83NDRUrVkS/fv2wbt06PH78OM+2C6uvly5dQq9evVCxYkVYWVnB3d0dwNNJpS/j+W17e3trfdd//PEHWrVqhXLlysHS0hL9+vXDvXv3lH3MHtnJz88//4zx48djz549yl+LauLn5wd7e3t88cUXedb56KOPcOrUKa3Xsz/nzxs8eDBOnTqFb775BikpKcqxJTMzE2vXrkXfvn2Vun379sWaNWteelQvm42NDbp27YrVq1cDeHoc+OWXX+Dn55ej7qJFi3Lsh4uLi07bzTZ79mwkJCQgMDAQNWrUQGBgIKpWrYqzZ88CePoz1rRpUxgaGuZYV9djVkpKCq5cuQI/Pz+t48qsWbO0jkfBwcEICAjIs+81atTA2rVrsWDBApiZmcHJyQkeHh5wdHRURnvatGmDL7/8EiNGjICxsTG8vLyUeVDPjgg9q2zZsti0aRO2bdsGCwsLWFtb4+HDh6hXr16u69y4cQO7d+/O8Z1lZWUhLS0NwcHBaNq0KZo3b45Vq1YhLCwMFy9e1KobHh6OY8eOITAwEIsXL8aGDRuUZV26dMHZs2dx+fJlzJgxA/v378eZM2cwbNgw9OzZE4sXL1Z+Zop7YrlBsW5d5SpXrgyNRoPz58+jS5cuOZafP38eZcuWVS4v1mg0OYLRsxPmkpOT8eabb2LdunU52ipbtqzyb3Nz8xzLO3ToAGNjY2zZsgVGRkZIT0/Hf/7zH113LU+5bTs/2Vcn/PbbbznC1rNDtc+3/TLrFZbnD6oajSbPXySWlpY4ceIE9u3bh99//x3Tpk3DjBkzcPToUZ0vJ9fT08v35wN4+j27ublh5cqVcHFxQVZWFmrWrFmoE9Wjo6Px3nvvYeTIkZg9ezbs7Oxw8OBB+Pn54cmTJzAzM8tx2jY3devWxYkTJ7B69WrUr1//lSYZl0QGBgaYPXs2Bg4ciDFjxuRap0yZMqhcuXKuyzw9PXHw4EGkp6crP3s2NjawsbHBjRs3tOru3r0bN2/eRI8ePbTKMzMzERoaitatW+u0D35+fmjVqhUuX76MsLAw6Ovro3v37jnqOTk55bkfr8Le3h7du3dH9+7dMWfOHNStWxfz58/H2rVrC/QzVhC5HVdWrlyZ448PfX39l2q3d+/e6N27N27dugVzc3NoNBosXLgQFStWVOpMmDAB48ePR3x8PGxtbREdHQ1/f3+tOs9r06YNrly5grt378LAwAA2NjZwcnLKdZ2goCDY29ujY8eOWuXOzs4wMDCAl5eXUpZ99V1sbCyqVKmilHt4eAB4+kf0rVu3MGPGDPTq1SvHttLS0jBq1Ch8//33uHz5MjIyMpQ/Yry8vBAZGYkOHToU5KMrEhzZKUL29vZo3bo1VqxYgX/++UdrWUJCAtatW6d13rds2bKIj49X3l+6dElrNKBevXq4dOkSHBwcULlyZa3Xiy69NTAwwIABAxAUFISgoCD07NmzwAcLKysruLi44NChQ1rlhw4dQvXq1fNcz9raGs7OzoiMjFTKMjIytM5HV69eHcbGxoiNjc2xT89fIfKsgqxXrVo1/Pnnn1rrHTly5IX7a2homOvckpdlYGAAHx8fzJs3D2fOnEF0dDT27t2ba91q1appfU659fX5nw8AWvfLuXfvHi5evIhPP/0UrVq1QrVq1fDgwQOd+v78to8cOaIcDI8fP46srCwsWLAADRs2hJeXF+Li4rTq16pVC6Ghofluo1KlSggLC8Ovv/6qdQWSmnTv3h01atTAZ5999tLr9urVC8nJyVixYsUL665atQo9e/bMMbrSs2dPrFq1SpeuAwBatGgBDw8PrePGy/5BU1iMjIxQqVIlZQ5RrVq1EB4enusVVLoesxwdHeHi4oKrV6/mOK5k/9J/WY6OjrCwsMCPP/4IExOTHMFTo9HAxcUFpqam2LBhA1xdXVGvXr0XtlumTBnY2Nhg7969uH37do5AIyIICgpC//79c/yh1rhxY2RkZGiNVkVFRQEA3Nzc8txm9ohQbmbNmoW2bduiXr16yMzM1LpiNj09vVCOqa+kWE+i/QtERUVJmTJlpGnTprJ//36JjY2VnTt3Ss2aNaVOnTpaV7D07NlTqlWrJidOnJCjR49Ky5YtxdDQUDlnnpKSIp6entK8eXM5cOCAXL16VcLCwuSDDz6Q69evi0ju8zqe7Yu+vr7o6+vLkSNHlPKCzNlZtGiRWFlZycaNG+XChQsyZcoUMTQ0lKioKBHJey7I3Llzxc7OTrZs2SLnz5+XoUOHiqWlpVYfP/nkE7G3t5c1a9bI5cuX5fjx47J06VJZs2aNiGjPg3jWi9aLiYkRIyMjmTRpkly4cEHWrVsnTk5OL5yz4+npKSNHjpT4+Hjl6qHc5nMsWrRIuVpLRHsuxLZt22TJkiVy8uRJiY6OlhUrVoienp6cO3cu121GRESInp6efPnllxIVFSXLli0TGxsbrTk7u3btEo1GI2vXrpWoqCiZNm2aWFlZKXN2MjMzxd7eXvr27SuXLl2S0NBQeeutt7TmIBV0zo6VlZV88cUXcvHiRVm+fLno6+vLrl27RETk1KlTylyHK1euSHBwsJQrV07rc7148aIYGRnJyJEj5fTp03L+/HlZsWKF3LlzJ8dndeHCBXFyctKaR7J582apUqVKnn3M7ufzc3Zu3LghVapUkcjISKXs+fkVebVd2HN2soWGhoqBgYEYGBjkmLMzc+ZMiY+P13o9e6XNxIkTRV9fX8aPHy/h4eESHR0tERER0rdvX9FoNJKYmCi3b98WQ0NDrSuNsu3YsUOMjY3l3r17SllB5+xk+/zzz8XW1lYAaB03sgGQoKCgHPuRnJyc5+f0ojk727Ztkz59+si2bdvk4sWLcuHCBfnyyy9FX19fgoODRUTk7t27Ym9vL127dpWjR49KVFSUBAcHK1cY6XrMWrlypZiamsqSJUvk4sWLcubMGVm9erUsWLBAqfOiOTsiIsuWLZPjx48r/4ey23zWvHnz5MyZM3Lu3DmZOXOmGBoaas0XzO3nefXq1RIRESGXL1+W77//Xuzs7GTChAk5tv/HH38IADl//nyOZZmZmVKvXj1555135MSJE3Ls2DFp0KCB1hV8y5cvl5CQEImKipKoqCj57rvvxNLSUj755JMc7f3111/i6empfOePHz8We3t7+e6772T79u1ibGyc7++X14Fh5zW4du2aMnFYo9EIAOnataukpKRo1bt586a0adNGzM3NxdPTU3bs2JFjUmN8fLz0799fypQpI8bGxlKxYkUZOnSocoDML+yIiDRt2jTHZejZB+hnL2F/XmZmpsyYMUPKlSsnhoaGeV7G+fyBIz09XcaOHStWVlZiY2MjEyZMyHHpeVZWlixevFiqVKkihoaGUrZsWfH19ZX9+/dr9e/5gPKi9USeHjSzL/9u2rSprF69+oVhJyQkRCpXriwGBgY5Lj1/Vn5hJzw8XJo1aya2trZiamoqtWrVkh9//DHPbYqIrFq1SsqXLy+mpqbSoUOHHJeei4hMmzZNHB0dxdraWsaPHy9jxozRmqC8Z88eqVatmhgbG0utWrVk3759OoWdzz77TLp37y5mZmbi5OSU4yC9cOFCcXZ2FlNTU/H19ZXg4OAcn+u+ffukUaNGYmxsLDY2NuLr66ssf/4X7t9//y0ODg7KQTv7FgH5yS3sZO/fs4GkWbNmWr9Y82q7qMKOiEibNm2UUPBsW3jmNgfZr+HDh2ut++OPP0rz5s3F2tpaDA0NpXz58tK7d28leMyfP19sbGxynaiblpYmNjY2Wt/fy4ad69evi56eXq63rxCRXPcBgAQEBOTxKb047Fy5ckWGDh0qXl5eyq0L3nrrLa3PT0Tk9OnT0qZNGzEzMxNLS0tp2rSpXLlyRUR0P2aJiKxbt07q1KkjRkZGYmtrK++8845s3rxZWf78z1Ru+vXrJ3Z2dmJkZCS1atVSQtqzWrRoIdbW1mJiYiINGjSQHTt2aC3P7ed5ypQp4ujoKIaGhuLp6SkLFizI9TYEvXr1kkaNGuXZv5s3b0rXrl3FwsJCHB0dZeDAgVqheOnSpVKjRg0xMzMTKysrqVu3rqxYsULrcnqRp8fhxo0by7Zt27TKt23bJhUqVBBHR0dZuXJlvp/V66ARKeB1z1Ropk+fjoULF2LPnj1o2LDha9uuiMDT0xOjRo3KcbdLomzu7u4YN25coT76oigUdj9nzJiBrVu35vsoDXd3d6xZswbNmzcvlG3+W82YMQPR0dFad5QmKkqcs1MMPvvsMyxduhRHjhzR+UqJl3Xnzh0sX74cCQkJWvdbICrNpkyZAgsLCyQmJurcRmxsLCwsLDBnzpxC7BkRlSS8GquYvO7A4eDggDJlyuDbb7+Fra3ta902UVHYv3+/Mjn1VR7T4eLioozmFNWVfERUvHgaC09nmMfFxcHS0lJ1l78SUeFZsWIF3n333XyvWKEXCw8PR2JiIt57773i7gqVciKCR48ewcXFJc/7EwEMOwCe3ngpv8uciYiIqOS6fv06ypcvn+dynsbC/4bAr1+/XnKe0EpERET5SkpKgqur6wtPZTPs4H8P1LOysmLYISIiKmVeNAWFV2MRERGRqjHsEBHRv8KuXbtQv3591KpVCw0bNsTp06cBAH/++ScaNmyIunXrolq1apg3b16ebURGRqJ27drw8vJCy5YtcfPmzdfVfXoFnKCMp+f8rK2tkZiYyNNYREQq9ODBA1SuXBkHDhxAjRo1EB4ejpEjR+LcuXOoU6cOZs6ciY4dO+L+/fuoWrUq9u3bl+M5WllZWfDy8sLKlSvRokULzJ8/H5GRkdi0aVMx7RUV9Pd3sY7sHDhwAB06dICLiws0Gg22bt2qtVxEMG3aNDg7O8PU1BQ+Pj64dOmSVp379++jT58+sLKygo2NDfz8/JQn1xIREQHAlStXYG9vjxo1agAAmjZtitjYWJw4cQIajQYPHz4EAKSkpMDIyAh2dnY52jh+/DgMDAzQokULAMDw4cOxbds2pKamvrb9IN0Ua9hJSUlB7dq18dVXX+W6fN68eVi6dCkCAwMRGRkJc3Nz+Pr6av1g9enTB3/99Rf27NmD7du348CBAxg2bNjr2gUiIioFPD09ce/ePRw+fBgAEBISgkePHiE6OhpBQUGYOnUqKlSoAC8vL8yZMwdOTk452oiNjdW6x5KlpSWsrKwQFxf32vaDdFOsV2O1a9cO7dq1y3WZiGDx4sX49NNP0alTJwBAcHAwHB0dsXXrVvTs2RPnz5/Hrl27cPToUdSvXx8AsGzZMrRv3x7z58+Hi4vLa9sXIiIquaytrfHzzz/D398fycnJ8Pb2RvXq1WFgYIC5c+ciICAAvXv3xtWrV9GsWTPUr18/x2ksKr1K7ATla9euISEhAT4+PkqZtbU1GjRogIiICABAREQEbGxslKADAD4+PtDT00NkZGSebaelpSEpKUnrRURE6taiRQvs378fx48fx4IFCxAXFwcXFxds2bIFvXv3BgBUrFgRDRs2xKFDh3KsX6FCBcTExCjvHz16hMTERP5hXQqU2LCTkJAAAHB0dNQqd3R0VJYlJCTAwcFBa7mBgQHs7OyUOrkJCAiAtbW18uLdk4mI1C8+Pl759+eff46WLVuibt26MDc3x969ewEAd+/eRWRkJGrWrJlj/TfffBPp6ekICwsDAHzzzTfo0KEDTExMXs8OkM7+lTcV9Pf3x4QJE5T32XdgJCIi9Zo2bRrCw8ORkZEBb29vrFq1Cvr6+vjpp5/w0UcfISMjA+np6Rg3bhy8vb0BAIGBgYiLi8PMmTOhp6eHH374AcOHD0dqaipcXFzw/fffF/NeUUGU2LCTPTns1q1bcHZ2Vspv3bqFOnXqKHVu376ttV5GRgbu37+f6+SybMbGxny6MRHRv8zKlStzLffx8cHx48dzXTZixAit997e3jhz5kyh942KVok9jeXh4QEnJyeEhoYqZUlJSYiMjFQSt7e3Nx4+fKj1Q7p3715kZWWhQYMGr73PREREVPIU68hOcnIyLl++rLy/du0aTp06BTs7O1SoUAHjxo3DrFmz4OnpCQ8PD0ydOhUuLi7o3LkzAKBatWpo27Ythg4disDAQKSnp2PMmDHo2bMnJ4wRERERgGIOO8eOHVNuzgRAmUczYMAArFmzBpMnT0ZKSgqGDRuGhw8fokmTJti1a5fWZLB169ZhzJgxaNWqFfT09NCtWzcsXbr0te8LERERlUx8XAT4uAgiKuVe8MRnomJXRFGjVDwugoiIiKioMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqleiwk5mZialTp8LDwwOmpqaoVKkSPv/8c4iIUkdEMG3aNDg7O8PU1BQ+Pj64dOlSMfaaiIiISpISHXa++OILfP3111i+fDnOnz+PL774AvPmzcOyZcuUOvPmzcPSpUsRGBiIyMhImJubw9fXF6mpqcXYcyIiIiopNPLsMEkJ895778HR0RGrVq1Syrp16wZTU1P88MMPEBG4uLhg4sSJmDRpEgAgMTERjo6OWLNmDXr27Fmg7SQlJcHa2hqJiYmwsrIqkn0hIioyGk1x94Aof0UUNQr6+7tEj+w0atQIoaGhiIqKAgCcPn0aBw8eRLt27QAA165dQ0JCAnx8fJR1rK2t0aBBA0REROTZblpaGpKSkrReREREpE4Gxd2B/Hz88cdISkpC1apVoa+vj8zMTMyePRt9+vQBACQkJAAAHB0dtdZzdHRUluUmICAAn332WdF1nIiIiEqMEj2y89NPP2HdunVYv349Tpw4gbVr12L+/PlYu3btK7Xr7++PxMRE5XX9+vVC6jERERGVNCV6ZOejjz7Cxx9/rMy9eeONNxATE4OAgAAMGDAATk5OAIBbt27B2dlZWe/WrVuoU6dOnu0aGxvD2Ni4SPtOREREJUOJHtl5/Pgx9PS0u6ivr4+srCwAgIeHB5ycnBAaGqosT0pKQmRkJLy9vV9rX4mIiKhkKtEjOx06dMDs2bNRoUIF1KhRAydPnsTChQsxePBgAIBGo8G4ceMwa9YseHp6wsPDA1OnToWLiws6d+5cvJ0nIiKiEqFEh51ly5Zh6tSpGDVqFG7fvg0XFxcMHz4c06ZNU+pMnjwZKSkpGDZsGB4+fIgmTZpg165dMDExKcaeExERUUlRou+z87rwPjtEVKrxPjtU0vE+O0RERERFh2GHiIiIVI1hh4iIiFSNYYeIiIhUjWGHiIiIVI1hh4iIiFSNYYeIiIhUjWGHiIiIVI1hh4iIiFSNYYeIiIhUjWGHiIiIVI1hh4iIiFSNYYeIiIhUjWGHiIiIVI1hh4iIiFSNYYeIiIhUjWGHiIiIVI1hh4iIiFSNYYeIiIhUjWGHiIiIVI1hh4iIiFSNYYeIiIhUjWGHiIiIVI1hh4iIiFSNYYeIiIhUjWGHiIiIVI1hh4iIiFSNYYeIiIhUjWGHiIiIVI1hh4iIiFSNYYeIiIhUjWGHiIiIVI1hh4iIiFSNYYeIiIhUjWGHiIiIVI1hh4iIiFSNYYeIiIhUjWGHiIiIVI1hh4iIiFSNYYeIiIhUjWGHiIiIVI1hh4iIiFSNYYeIiIhUjWGHiIiIVI1hh4iIiFSNYYeIiIhUjWGHiIiIVI1hh4iIiFSNYYeIiIhUjWGHiIiIVI1hh4iIiFRNp7Bz9erVwu4HERERUZHQKexUrlwZLVq0wA8//IDU1NTC7hMRERFRodEp7Jw4cQK1atXChAkT4OTkhOHDh+PPP/8s7L4BAG7evIm+ffvC3t4epqameOONN3Ds2DFluYhg2rRpcHZ2hqmpKXx8fHDp0qUi6QsRERGVPjqFnTp16mDJkiWIi4vD6tWrER8fjyZNmqBmzZpYuHAh7ty5Uyide/DgARo3bgxDQ0Ps3LkTf//9NxYsWABbW1ulzrx587B06VIEBgYiMjIS5ubm8PX15YgTERERAQA0IiKv2khaWhpWrFgBf39/PHnyBEZGRnj//ffxxRdfwNnZWed2P/74Yxw6dAjh4eG5LhcRuLi4YOLEiZg0aRIAIDExEY6OjlizZg169uyZZ3/T0tKU90lJSXB1dUViYiKsrKx07i8RUbHQaIq7B0T5e/WokaukpCRYW1u/8Pf3K12NdezYMYwaNQrOzs5YuHAhJk2ahCtXrmDPnj2Ii4tDp06dXqV5hISEoH79+ujevTscHBxQt25drFy5Ull+7do1JCQkwMfHRymztrZGgwYNEBERkWe7AQEBsLa2Vl6urq6v1E8iIiIquXQKOwsXLsQbb7yBRo0aIS4uDsHBwYiJicGsWbPg4eGBpk2bYs2aNThx4sQrde7q1av4+uuv4enpid27d2PkyJH48MMPsXbtWgBAQkICAMDR0VFrPUdHR2VZbvz9/ZGYmKi8rl+//kr9JCIiopLLQJeVvv76awwePBgDBw7M8zSVg4MDVq1a9Uqdy8rKQv369TFnzhwAQN26dXHu3DkEBgZiwIABOrdrbGwMY2PjV+obERERlQ46hZ2CXO1kZGT0SoEEAJydnVG9enWtsmrVquGXX34BADg5OQEAbt26pRW6bt26hTp16rzStomIiEgddDqNFRQUhE2bNuUo37Rpk3KKqTA0btwYFy9e1CqLioqCm5sbAMDDwwNOTk4IDQ1VliclJSEyMhLe3t6F1g8iIiIqvXQKOwEBAShTpkyOcgcHB+WUU2EYP348jhw5gjlz5uDy5ctYv349vv32W4wePRoAoNFoMG7cOMyaNQshISE4e/Ys+vfvDxcXF3Tu3LnQ+kFERESll06nsWJjY+Hh4ZGj3M3NDbGxsa/cqWxvvfUWtmzZAn9/f8ycORMeHh5YvHgx+vTpo9SZPHkyUlJSMGzYMDx8+BBNmjTBrl27YGJiUmj9ICIiotJLp/vsVKhQAcuXL0fHjh21yn/99VeMHj0aN27cKLQOvg4FvU6fiKhE4n12qKQrjffZ6dWrFz788EOEhYUhMzMTmZmZ2Lt3L8aOHZvnjfyIiIiIioNOp7E+//xzREdHo1WrVjAweNpEVlYW+vfvX6hzdoiIiIhe1Ss9LiIqKgqnT59WHtCZfZVUacPTWERUqvE0FpV0xXwaS6eRnWxeXl7w8vJ6lSaIiIiIipROYSczMxNr1qxBaGgobt++jaysLK3le/fuLZTOEREREb0qncLO2LFjsWbNGrz77ruoWbMmNBxCJSIiohJKp7CzceNG/PTTT2jfvn1h94eIiIioUOl06bmRkREqV65c2H0hIiIiKnQ6hZ2JEydiyZIleIULuYiIiIheC51OYx08eBBhYWHYuXMnatSoAUNDQ63lmzdvLpTOEREREb0qncKOjY0NunTpUth9ISIiIip0OoWdoKCgwu4HERERUZHQac4OAGRkZOCPP/7AN998g0ePHgEA4uLikJycXGidIyIiInpVOo3sxMTEoG3btoiNjUVaWhpat24NS0tLfPHFF0hLS0NgYGBh95OIiIhIJzqN7IwdOxb169fHgwcPYGpqqpR36dIFoaGhhdY5IiIiolel08hOeHg4Dh8+DCMjI61yd3d33Lx5s1A6RkRERFQYdBrZycrKQmZmZo7yGzduwNLS8pU7RURERFRYdAo7bdq0weLFi5X3Go0GycnJmD59Oh8hQURERCWKRnS4DfKNGzfg6+sLEcGlS5dQv359XLp0CWXKlMGBAwfg4OBQFH0tMklJSbC2tkZiYiKsrKyKuztERC+HD2Omkq6InrhQ0N/fOoUd4Oml5xs3bsSZM2eQnJyMevXqoU+fPloTlksLhh0iKtUYdqikK+awo9MEZQAwMDBA3759dV2diIiI6LXQKewEBwfnu7x///46dYaIiIiosOl0GsvW1lbrfXp6Oh4/fgwjIyOYmZnh/v37hdbB14GnsYioVONpLCrpivk0lk5XYz148EDrlZycjIsXL6JJkybYsGGDzp0mIiIiKmw6PxvreZ6enpg7dy7Gjh1bWE0SERERvbJCCzvA00nLcXFxhdkkERER0SvRaYJySEiI1nsRQXx8PJYvX47GjRsXSseIiIiICoNOYadz585a7zUaDcqWLYuWLVtiwYIFhdEvIiIiokKhU9jJysoq7H4QERERFYlCnbNDREREVNLoNLIzYcKEAtdduHChLpsgIiIiKhQ6hZ2TJ0/i5MmTSE9PR5UqVQAAUVFR0NfXR7169ZR6Gt7oioiIiIqZTmGnQ4cOsLS0xNq1a5W7KT948ACDBg1C06ZNMXHixELtJBEREZGudHpcRLly5fD777+jRo0aWuXnzp1DmzZtSt29dvi4CCIq1TiKTiVdaXxcRFJSEu7cuZOj/M6dO3j06JEuTRIREREVCZ3CTpcuXTBo0CBs3rwZN27cwI0bN/DLL7/Az88PXbt2Lew+EhEREelMpzk7gYGBmDRpEnr37o309PSnDRkYwM/PD19++WWhdpCIiIjoVeg0ZydbSkoKrly5AgCoVKkSzM3NC61jrxPn7BBRqcY5O1TSlcY5O9ni4+MRHx8PT09PmJub4xVyExEREVGR0Cns3Lt3D61atYKXlxfat2+P+Ph4AICfnx8vOyciIqISRaewM378eBgaGiI2NhZmZmZKeY8ePbBr165C6xwRERHRq9JpgvLvv/+O3bt3o3z58lrlnp6eiImJKZSOERERERUGnUZ2UlJStEZ0st2/fx/Gxsav3CkiIiKiwqJT2GnatCmCg4OV9xqNBllZWZg3bx5atGhRaJ0jIiIielU6ncaaN28eWrVqhWPHjuHJkyeYPHky/vrrL9y/fx+HDh0q7D4SERER6UynkZ2aNWsiKioKTZo0QadOnZCSkoKuXbvi5MmTqFSpUmH3kYiIiEhnLz2yk56ejrZt2yIwMBCffPJJUfSJiIiIqNC89MiOoaEhzpw5UxR9ISIiIip0Op3G6tu3L1atWlXYfSEiIiIqdDpNUM7IyMDq1avxxx9/4M0338zxTKyFCxcWSueIiIiIXtVLhZ2rV6/C3d0d586dQ7169QAAUVFRWnU0fCAdERERlSAvFXY8PT0RHx+PsLAwAE8fD7F06VI4OjoWSeeIiIiIXtVLzdl5/qnmO3fuREpKSqF2KD9z586FRqPBuHHjlLLU1FSMHj0a9vb2sLCwQLdu3XDr1q3X1iciIiIq2XSaoJzt+fBTlI4ePYpvvvkGtWrV0iofP348tm3bhk2bNmH//v2Ii4tD165dX1u/iIiIqGR7qbCj0WhyzMl5HXN0kpOT0adPH6xcuRK2trZKeWJiIlatWoWFCxeiZcuWePPNNxEUFITDhw/jyJEjRd4vIiIiKvleas6OiGDgwIHKwz5TU1MxYsSIHFdjbd68ufB6CGD06NF499134ePjg1mzZinlx48fR3p6Onx8fJSyqlWrokKFCoiIiEDDhg1zbS8tLQ1paWnK+6SkpELtLxEREZUcLxV2BgwYoPW+b9++hdqZ3GzcuBEnTpzA0aNHcyxLSEiAkZERbGxstModHR2RkJCQZ5sBAQH47LPPCrurREREVAK9VNgJCgoqqn7k6vr16xg7diz27NkDExOTQmvX398fEyZMUN4nJSXB1dW10NonIiKikuOVJigXtePHj+P27duoV68eDAwMYGBggP3792Pp0qUwMDCAo6Mjnjx5gocPH2qtd+vWLTg5OeXZrrGxMaysrLReREREpE463UH5dWnVqhXOnj2rVTZo0CBUrVoVU6ZMgaurKwwNDREaGopu3boBAC5evIjY2Fh4e3sXR5eJiIiohCnRYcfS0hI1a9bUKjM3N4e9vb1S7ufnhwkTJsDOzg5WVlb44IMP4O3tnefkZCIiIvp3KdFhpyAWLVoEPT09dOvWDWlpafD19cWKFSuKu1tERERUQmjkdd4ZsIRKSkqCtbU1EhMTOX+HiEofPpOQSroiihoF/f1doicoExEREb0qhh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJStRIddgICAvDWW2/B0tISDg4O6Ny5My5evKhVJzU1FaNHj4a9vT0sLCzQrVs33Lp1q5h6TERERCVNiQ47+/fvx+jRo3HkyBHs2bMH6enpaNOmDVJSUpQ648ePx7Zt27Bp0ybs378fcXFx6Nq1azH2moiIiEoSjYhIcXeioO7cuQMHBwfs378f77zzDhITE1G2bFmsX78e//nPfwAAFy5cQLVq1RAREYGGDRvm2k5aWhrS0tKU90lJSXB1dUViYiKsrKxey74QERUajaa4e0CUvyKKGklJSbC2tn7h7+8SPbLzvMTERACAnZ0dAOD48eNIT0+Hj4+PUqdq1aqoUKECIiIi8mwnICAA1tbWysvV1bVoO05ERETFptSEnaysLIwbNw6NGzdGzZo1AQAJCQkwMjKCjY2NVl1HR0ckJCTk2Za/vz8SExOV1/Xr14uy60RERFSMDIq7AwU1evRonDt3DgcPHnzltoyNjWFsbFwIvSIiIqKSrlSM7IwZMwbbt29HWFgYypcvr5Q7OTnhyZMnePjwoVb9W7duwcnJ6TX3koiIiEqiEh12RARjxozBli1bsHfvXnh4eGgtf/PNN2FoaIjQ0FCl7OLFi4iNjYW3t/fr7i4RERGVQCX6NNbo0aOxfv16/Prrr7C0tFTm4VhbW8PU1BTW1tbw8/PDhAkTYGdnBysrK3zwwQfw9vbO80osIiIi+ncp0Zeea/K4nDIoKAgDBw4E8PSmghMnTsSGDRuQlpYGX19frFix4qVOYxX00jUiohKJl55TSVfMl56X6LDzujDsEFGpxrBDJR3vs0NERERUdBh2iIiISNUYdoiIiEjVGHaIiIhI1Rh2iIiISNUYdoiIiEjVGHaIiIhI1Rh2iIiISNUYdoiIiEjVGHaIiIhI1Rh2iIiISNUYdoiIiEjVGHaIiIhI1Rh2iIiISNUYdoiIiEjVGHaIiIhI1Rh2iIiISNUYdoiIiEjVGHaIiIhI1Rh2iIiISNUYdoiIiEjVGHaIiIhI1Rh2iIiISNUYdoiIiEjVGHaIiIhI1Rh2iIiISNUYdoiIiEjVGHaIiIhI1Rh2iIiISNUYdoiIiEjVGHaIiIhI1Rh2iIiISNUYdoiIiEjVGHaIiIhI1Rh2iIiISNUYdoiIiEjVGHaIiIhI1Rh2qMRJTU1F586d4eXlhdq1a6N169a4fPlyrnW3b9+OqlWrwtPTE127dkVSUtJr7i0REZV0DDtUIg0bNgwXL17E6dOn0alTJwwZMiRHneTkZPj5+WHr1q24dOkSXFxc8PnnnxdDb4mIqCRj2KESx8TEBO3bt4dGowEANGzYENHR0Tnq7dy5E3Xr1kXVqlUBAKNGjcKGDRteZ1eJiKgUYNihEm/JkiXo1KlTjvLY2Fi4ubkp793d3REfH4+MjIzX2T0iIirhDIq7A0T5mTNnDi5fvozQ0NDi7goREZVSHNmhEmv+/PnYvHkzdu7cCTMzsxzLK1SogJiYGOV9dHQ0nJ2dYWDADE9ERP/DsEMl0sKFC7Fhwwbs2bMHNjY2udZp27YtTpw4gQsXLgAAVqxYgZ49e77GXhIRUWmgEREp7k4Ut6SkJFhbWyMxMRFWVlbF3Z1/vRs3bsDV1RUVK1aEpaUlAMDY2BiRkZGYNm0aXFxcMGLECABASEgIJk+ejIyMDNSsWRNr166FtbV1cXaf6PX7/8n8RCVWEUWNgv7+ZtgBww4RlXIMO1TSFXPY4WksIiIiUjWGHSIiIlI1hh0iIiJSNV6jW8Q0n/FcOpVsMv1fP22PiFSOIztERESkagw7REREpGqqCTtfffUV3N3dYWJiggYNGuDPP/8s7i4RERFRCaCKsPPjjz9iwoQJmD59Ok6cOIHatWvD19cXt2/fLu6uERERUTFTRdhZuHAhhg4dikGDBqF69eoIDAyEmZkZVq9eXdxdIyIiomJW6q/GevLkCY4fPw5/f3+lTE9PDz4+PoiIiMh1nbS0NKSlpSnvExMTATy9E2OhSy38JokKU5H83BMRPauIjjPZx68XPQyi1Iedu3fvIjMzE46Ojlrljo6OygMinxcQEIDPPvssR7mrq2uR9JGoJLOey2eJEVERK+JnFj569Cjf5yKW+rCjC39/f0yYMEF5n5WVhfv378Pe3h4aPmOmREtKSoKrqyuuX7/O55gRUZHgcab0EBE8evQILi4u+dYr9WGnTJky0NfXx61bt7TKb926BScnp1zXMTY2hrGxsVaZjY1NUXWRioCVlRUPQkRUpHicKR3yG9HJVuonKBsZGeHNN99EaGioUpaVlYXQ0FB4e3sXY8+IiIioJCj1IzsAMGHCBAwYMAD169fH22+/jcWLFyMlJQWDBg0q7q4RERFRMVNF2OnRowfu3LmDadOmISEhAXXq1MGuXbtyTFqm0s/Y2BjTp0/PcRqSiKiw8DijPhp50fVaRERERKVYqZ+zQ0RERJQfhh0iIiJSNYYdIiIiUjWGHSrx9u3bB41Gg4cPH+Zbz93dHYsXL34tfSIiysZjT8nHsEOFZuDAgdBoNNBoNDAyMkLlypUxc+ZMZGRkvFK7jRo1Qnx8vHLjqDVr1uR6E8ijR49i2LBhr7QtIipZso8rc+fO1SrfunXra7/jPY89pRfDDhWqtm3bIj4+HpcuXcLEiRMxY8YMfPnll6/UppGREZycnF54YCtbtizMzMxeaVtEVPKYmJjgiy++wIMHD4q7K7nisafkY9ihQmVsbAwnJye4ublh5MiR8PHxQUhICB48eID+/fvD1tYWZmZmaNeuHS5duqSsFxMTgw4dOsDW1hbm5uaoUaMGduzYAUD7NNa+ffswaNAgJCYmKqNIM2bMAKA9lNy7d2/06NFDq2/p6ekoU6YMgoODATy903ZAQAA8PDxgamqK2rVr4+effy76D4mIXoqPjw+cnJwQEBCQZ52DBw+iadOmMDU1haurKz788EOkpKQoy+Pj4/Huu+/C1NQUHh4eWL9+fY7TTwsXLsQbb7wBc3NzuLq6YtSoUUhOTgYAHntKOYYdKlKmpqZ48uQJBg4ciGPHjiEkJAQREREQEbRv3x7p6ekAgNGjRyMtLQ0HDhzA2bNn8cUXX8DCwiJHe40aNcLixYthZWWF+Ph4xMfHY9KkSTnq9enTB9u2bVMOVACwe/duPH78GF26dAEABAQEIDg4GIGBgfjrr78wfvx49O3bF/v37y+iT4OIdKGvr485c+Zg2bJluHHjRo7lV65cQdu2bdGtWzecOXMGP/74Iw4ePIgxY8Yodfr374+4uDjs27cPv/zyC7799lvcvn1bqx09PT0sXboUf/31F9auXYu9e/di8uTJAHjsKfWEqJAMGDBAOnXqJCIiWVlZsmfPHjE2NpbOnTsLADl06JBS9+7du2Jqaio//fSTiIi88cYbMmPGjFzbDQsLEwDy4MEDEREJCgoSa2vrHPXc3Nxk0aJFIiKSnp4uZcqUkeDgYGV5r169pEePHiIikpqaKmZmZnL48GGtNvz8/KRXr1667D4RFYFnjysNGzaUwYMHi4jIli1bJPtXmJ+fnwwbNkxrvfDwcNHT05N//vlHzp8/LwDk6NGjyvJLly4JAOWYkZtNmzaJvb298p7HntJLFY+LoJJj+/btsLCwQHp6OrKystC7d2907doV27dvR4MGDZR69vb2qFKlCs6fPw8A+PDDDzFy5Ej8/vvv8PHxQbdu3VCrVi2d+2FgYID3338f69atQ79+/ZCSkoJff/0VGzduBABcvnwZjx8/RuvWrbXWe/LkCerWravzdomo6HzxxRdo2bJljhGV06dP48yZM1i3bp1SJiLIysrCtWvXEBUVBQMDA9SrV09ZXrlyZdja2mq188cffyAgIAAXLlxAUlISMjIykJqaisePHxd4Tg6PPSUTww4VqhYtWuDrr7+GkZERXFxcYGBggJCQkBeuN2TIEPj6+uK3337D77//joCAACxYsAAffPCBzn3p06cPmjVrhtu3b2PPnj0wNTVF27ZtAUAZYv7tt99Qrlw5rfX4PByikumdd96Br68v/P39MXDgQKU8OTkZw4cPx4cffphjnQoVKiAqKuqFbUdHR+O9997DyJEjMXv2bNjZ2eHgwYPw8/PDkydPXmoCMo89JQ/DDhUqc3NzVK5cWausWrVqyMjIQGRkJBo1agQAuHfvHi5evIjq1asr9VxdXTFixAiMGDEC/v7+WLlyZa5hx8jICJmZmS/sS6NGjeDq6ooff/wRO3fuRPfu3WFoaAgAqF69OoyNjREbG4tmzZq9yi4T0Ws0d+5c1KlTB1WqVFHK6tWrh7///jvHsSdblSpVkJGRgZMnT+LNN98E8HSE5dmru44fP46srCwsWLAAenpPp7P+9NNPWu3w2FN6MexQkfP09ESnTp0wdOhQfPPNN7C0tMTHH3+McuXKoVOnTgCAcePGoV27dvDy8sKDBw8QFhaGatWq5dqeu7s7kpOTERoaitq1a8PMzCzPv7p69+6NwMBAREVFISwsTCm3tLTEpEmTMH78eGRlZaFJkyZITEzEoUOHYGVlhQEDBhT+B0FEr+yNN95Anz59sHTpUqVsypQpaNiwIcaMGYMhQ4bA3Nwcf//9N/bs2YPly5ejatWq8PHxwbBhw/D111/D0NAQEydOhKmpqXJLi8qVKyM9PR3Lli1Dhw4dcOjQIQQGBmptm8eeUqy4Jw2Rejw7kfB59+/fl379+om1tbWYmpqKr6+vREVFKcvHjBkjlSpVEmNjYylbtqz069dP7t69KyI5JyiLiIwYMULs7e0FgEyfPl1EtCcJZvv7778FgLi5uUlWVpbWsqysLFm8eLFUqVJFDA0NpWzZsuLr6yv79+9/5c+CiApHbseVa9euiZGRkTz7K+zPP/+U1q1bi4WFhZibm0utWrVk9uzZyvK4uDhp166dGBsbi5ubm6xfv14cHBwkMDBQqbNw4UJxdnZWjlHBwcE89qiERkSkGLMWERHRa3fjxg24urrijz/+QKtWrYq7O1TEGHaIiEj19u7di+TkZLzxxhuIj4/H5MmTcfPmTURFRSnzaUi9OGeHiIhULz09Hf/9739x9epVWFpaolGjRli3bh2Dzr8ER3aIiIhI1fi4CCIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIlKdffv2QaPR4OHDh8XdFSIqARh2iKjI3LlzByNHjkSFChVgbGwMJycn+Pr64tChQ4W2jebNm2PcuHFaZY0aNUJ8fDysra0LbTu6GjhwIDp37lzc3SD6V+NNBYmoyHTr1g1PnjzB2rVrUbFiRdy6dQuhoaG4d+9ekW7XyMgITk5ORboNIipFivPBXESkXg8ePBAAsm/fvnzr+Pn5SZkyZcTS0lJatGghp06dUpZPnz5dateuLcHBweLm5iZWVlbSo0cPSUpKEpGnD4kEoPW6du1ajofHBgUFibW1tWzbtk28vLzE1NRUunXrJikpKbJmzRpxc3MTGxsb+eCDDyQjI0PZfmpqqkycOFFcXFzEzMxM3n77bQkLC1OWZ7e7a9cuqVq1qpibm4uvr6/ExcUp/X++f8+uT0SvB09jEVGRsLCwgIWFBbZu3Yq0tLRc63Tv3h23b9/Gzp07cfz4cdSrVw+tWrXC/fv3lTpXrlzB1q1bsX37dmzfvh379+/H3LlzAQBLliyBt7c3hg4divj4eMTHx8PV1TXXbT1+/BhLly7Fxo0bsWvXLuzbtw9dunTBjh07sGPHDnz//ff45ptv8PPPPyvrjBkzBhEREdi4cSPOnDmD7t27o23btrh06ZJWu/Pnz8f333+PAwcOIDY2FpMmTQIATJo0Ce+//z7atm2r9K9Ro0av/NkS0Usq7rRFROr1888/i62trZiYmEijRo3E399fTp8+LSIi4eHhYmVlJampqVrrVKpUSb755hsReToyYmZmpozkiIh89NFH0qBBA+V9s2bNZOzYsVpt5DayA0AuX76s1Bk+fLiYmZnJo0ePlDJfX18ZPny4iIjExMSIvr6+3Lx5U6vtVq1aib+/f57tfvXVV+Lo6Ki8HzBggHTq1KlAnxcRFQ3O2SGiItOtWze8++67CA8Px5EjR7Bz507MmzcP3333HVJSUpCcnAx7e3utdf755x9cuXJFee/u7g5LS0vlvbOzM27fvv3SfTEzM0OlSpWU946OjnB3d4eFhYVWWXbbZ8+eRWZmJry8vLTaSUtL0+rz8+3q2j8iKjoMO0RUpExMTNC6dWu0bt0aU6dOxZAhQzB9+nSMGjUKzs7O2LdvX451bGxslH8//1RqjUaDrKysl+5Hbu3k13ZycjL09fVx/Phx6Ovra9V7NiDl1obw+cpEJQrDDhG9VtWrV8fWrVtRr149JCQkwMDAAO7u7jq3Z2RkhMzMzMLr4P+rW7cuMjMzcfv2bTRt2lTndoqqf0RUcJygTERF4t69e2jZsiV++OEHnDlzBteuXcOmTZswb948dOrUCT4+PvD29kbnzp3x+++/Izo6GocPH8Ynn3yCY8eOFXg77u7uiIyMRHR0NO7evavTqE9uvLy80KdPH/Tv3x+bN2/GtWvX8OeffyIgIAC//fbbS/XvzJkzuHjxIu7evYv09PRC6R8RFRzDDhEVCQsLCzRo0ACLFi3CO++8g5o1a2Lq1KkYOnQoli9fDo1Ggx07duCdd97BoEGD4OXlhZ49eyImJgaOjo4F3s6kSZOgr6+P6tWro2zZsoiNjS20fQgKCkL//v0xceJEVKlSBZ07d8bRo0dRoUKFArcxdOhQVKlSBfXr10fZsmUL9YaKRFQwGuHJZSIiIlIxjuwQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkaox7BAREZGqMewQERGRqjHsEBERkar9H94pR6HN9YKGAAAAAElFTkSuQmCC", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -466,7 +435,17 @@ }, { "data": { - "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -499,6 +478,8 @@ "print(f\"Lowest positive query: {smallest_positive_query}\")\n", "print(f\"Lowest negative query: {smallest_negative_query}\")\n", "\n", + "create_boxplot(percentages)\n", + "\n", "create_bar_chart(\n", " positive_count=highest_positive_query[2],\n", " negative_count=100 - highest_positive_query[2],\n", @@ -534,14 +515,14 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 104, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "61.28\n" + "62.682\n" ] } ],