diff --git "a/finetuned-models/finetuned_on_issue/finetuning_on_issues.ipynb" "b/finetuned-models/finetuned_on_issue/finetuning_on_issues.ipynb"
new file mode 100644--- /dev/null
+++ "b/finetuned-models/finetuned_on_issue/finetuning_on_issues.ipynb"
@@ -0,0 +1,674 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "844df125-fd09-4a5c-90b3-dcf06f8ad136",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be1299c7ca4c4112bd020faee58a6233",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from huggingface_hub import notebook_login\n",
+    "\n",
+    "notebook_login()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "22974cf4-0191-4c90-a7dd-c12ccaba510a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset, load_metric\n",
+    "from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer, DataCollatorWithPadding\n",
+    "import numpy as np\n",
+    "\n",
+    "# Load your dataset\n",
+    "data_files = {\"train\": \"../../data_splits/train-data-split.csv\", \"test\": \"../../data_splits/test-data-split.csv\"}\n",
+    "dataset = load_dataset(\"csv\", data_files=data_files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bea652f0-97e0-4247-97db-47c2669348c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['Consumer complaint narrative', 'Product', 'Sub-product', 'Issue', 'Sub-issue'],\n",
+       "        num_rows: 185637\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['Consumer complaint narrative', 'Product', 'Sub-product', 'Issue', 'Sub-issue'],\n",
+       "        num_rows: 61880\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "75c5eb50-f0e4-4c4b-946d-a51f5ebd62dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "issue_categories = np.unique(dataset['train']['Issue'])\n",
+    "issue_mapping = {k : i for i,k in enumerate(issue_categories)}\n",
+    "\n",
+    "def encode_categories(batch):\n",
+    "    return {\"labels\": [issue_mapping[category] for category in batch['Issue']]}\n",
+    "    \n",
+    "dataset['train'] = dataset['train'].map(encode_categories , batched=True)\n",
+    "dataset['test'] = dataset['test'].map(encode_categories, batched=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ca2f6927-e2b7-4112-a1bb-f0843abcb952",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['Consumer complaint narrative', 'Product', 'Sub-product', 'Issue', 'Sub-issue', 'labels'],\n",
+       "        num_rows: 185637\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['Consumer complaint narrative', 'Product', 'Sub-product', 'Issue', 'Sub-issue', 'labels'],\n",
+       "        num_rows: 61880\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4a50b127-0f66-4eb8-a548-5db862c4b099",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoTokenizer\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert/distilbert-base-uncased\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "659cceef-cfc8-4d34-ab46-ce4e372e9f91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def preprocess_function(examples):\n",
+    "    return tokenizer(examples[\"Consumer complaint narrative\"], truncation=True, padding=\"max_length\", max_length=512)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1983f660-2ec8-4e0b-900b-f34d94cd902d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenized_data = dataset.map(preprocess_function, batched=True, remove_columns=['Product', 'Sub-product','Issue', 'Sub-issue'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "580a3b23-bc05-4f53-8c68-a8037efcfdc1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['Consumer complaint narrative', 'labels', 'input_ids', 'attention_mask'],\n",
+       "        num_rows: 185637\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['Consumer complaint narrative', 'labels', 'input_ids', 'attention_mask'],\n",
+       "        num_rows: 61880\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenized_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4dd01f79-43dd-42b3-a2f8-4a61b277a2ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenized_data = tokenized_data.remove_columns('Consumer complaint narrative')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8332e18c-684d-452f-b7f2-2ee55ed82479",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['labels', 'input_ids', 'attention_mask'],\n",
+       "        num_rows: 185637\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['labels', 'input_ids', 'attention_mask'],\n",
+       "        num_rows: 61880\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenized_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0dc49e33-3060-43cc-a15e-ecf139a8278f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import DataCollatorWithPadding\n",
+    "\n",
+    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "40f5fb35-1c18-4deb-a729-e85461a5a105",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import evaluate\n",
+    "\n",
+    "def compute_metrics(eval_pred):\n",
+    "    predictions, labels = eval_pred\n",
+    "    predictions = np.argmax(predictions, axis=1)\n",
+    "    result = {}\n",
+    "\n",
+    "    metric_f1 = load_metric(\"f1\")\n",
+    "    metric_precision = load_metric(\"precision\")\n",
+    "    metric_recall = load_metric(\"recall\")\n",
+    "    metric_acc = load_metric(\"accuracy\")\n",
+    "    \n",
+    "    result[\"accuracy\"] = metric_acc.compute(predictions = predictions, references = labels)[\"accuracy\"]\n",
+    "    result[\"precision\"] = metric_precision.compute(predictions = predictions, references = labels,average = 'macro')['precision']\n",
+    "    result[\"recall\"] = metric_recall.compute(predictions = predictions, references = labels,average = 'macro')[\"recall\"]\n",
+    "    result[\"f1\"] = metric_f1.compute(predictions = predictions, references = labels, average = 'macro')[\"f1\"]\n",
+    "    \n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3b078fd2-7eb7-4a28-84e3-a359fe9794a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "id2label = {v : k for k,v in issue_mapping.items()}\n",
+    "label2id = issue_mapping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8063133f-be13-4d9e-93b2-8c9966c38c07",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert/distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
+    "\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(\n",
+    "    \"distilbert/distilbert-base-uncased\", num_labels=len(id2label), id2label=id2label, label2id=label2id\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ed2ba4a2-792f-47f9-bd07-f34427c44c34",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected kernel version 3.10.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
+     ]
+    }
+   ],
+   "source": [
+    "training_args = TrainingArguments(\n",
+    "    output_dir=\"distil-bert-fintuned-issues-cfpb-complaints\",\n",
+    "    learning_rate=2e-5,\n",
+    "    per_device_train_batch_size=16,\n",
+    "    per_device_eval_batch_size=16,\n",
+    "    num_train_epochs=2,\n",
+    "    weight_decay=0.01,\n",
+    "    evaluation_strategy=\"epoch\",\n",
+    "    save_strategy=\"epoch\",\n",
+    "    load_best_model_at_end=True,\n",
+    "    push_to_hub=True,\n",
+    ")\n",
+    "\n",
+    "trainer = Trainer(\n",
+    "    model=model,\n",
+    "    args=training_args,\n",
+    "    train_dataset=tokenized_data[\"train\"],\n",
+    "    eval_dataset=tokenized_data[\"test\"],\n",
+    "    tokenizer=tokenizer,\n",
+    "    data_collator=data_collator,\n",
+    "    compute_metrics=compute_metrics,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "9e3f1e85-dca4-44e2-9295-388c490c9d21",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='23206' max='23206' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [23206/23206 1:20:01, Epoch 2/2]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Epoch</th>\n",
+       "      <th>Training Loss</th>\n",
+       "      <th>Validation Loss</th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>Precision</th>\n",
+       "      <th>Recall</th>\n",
+       "      <th>F1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>0.726900</td>\n",
+       "      <td>0.718676</td>\n",
+       "      <td>0.743762</td>\n",
+       "      <td>0.585285</td>\n",
+       "      <td>0.532749</td>\n",
+       "      <td>0.549084</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.612000</td>\n",
+       "      <td>0.671110</td>\n",
+       "      <td>0.765175</td>\n",
+       "      <td>0.593483</td>\n",
+       "      <td>0.557717</td>\n",
+       "      <td>0.570207</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_36934/3516474932.py:9: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
+      "  metric_f1 = load_metric(\"f1\")\n",
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/datasets/load.py:753: FutureWarning: The repository for f1 contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.17.1/metrics/f1/f1.py\n",
+      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
+      "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
+      "  warnings.warn(\n",
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/datasets/load.py:753: FutureWarning: The repository for precision contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.17.1/metrics/precision/precision.py\n",
+      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
+      "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
+      "  warnings.warn(\n",
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/datasets/load.py:753: FutureWarning: The repository for recall contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.17.1/metrics/recall/recall.py\n",
+      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
+      "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
+      "  warnings.warn(\n",
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/datasets/load.py:753: FutureWarning: The repository for accuracy contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.17.1/metrics/accuracy/accuracy.py\n",
+      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
+      "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
+      "  warnings.warn(\n",
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "Checkpoint destination directory distil-bert-fintuned-issues-cfpb-complaints/checkpoint-11603 already exists and is non-empty.Saving will proceed but saved results may be invalid.\n",
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/datasets/load.py:753: FutureWarning: The repository for f1 contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.17.1/metrics/f1/f1.py\n",
+      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
+      "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
+      "  warnings.warn(\n",
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/datasets/load.py:753: FutureWarning: The repository for precision contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.17.1/metrics/precision/precision.py\n",
+      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
+      "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
+      "  warnings.warn(\n",
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/datasets/load.py:753: FutureWarning: The repository for recall contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.17.1/metrics/recall/recall.py\n",
+      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
+      "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
+      "  warnings.warn(\n",
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/datasets/load.py:753: FutureWarning: The repository for accuracy contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.17.1/metrics/accuracy/accuracy.py\n",
+      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
+      "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
+      "  warnings.warn(\n",
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TrainOutput(global_step=23206, training_loss=0.7534095095445665, metrics={'train_runtime': 4801.6859, 'train_samples_per_second': 77.322, 'train_steps_per_second': 4.833, 'total_flos': 4.919485725586022e+16, 'train_loss': 0.7534095095445665, 'epoch': 2.0})"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ba1fe347-89c3-4e55-a066-ae4fbca716d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/datasets/load.py:753: FutureWarning: The repository for f1 contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.17.1/metrics/f1/f1.py\n",
+      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
+      "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
+      "  warnings.warn(\n",
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/datasets/load.py:753: FutureWarning: The repository for precision contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.17.1/metrics/precision/precision.py\n",
+      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
+      "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
+      "  warnings.warn(\n",
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/datasets/load.py:753: FutureWarning: The repository for recall contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.17.1/metrics/recall/recall.py\n",
+      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
+      "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
+      "  warnings.warn(\n",
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/datasets/load.py:753: FutureWarning: The repository for accuracy contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.17.1/metrics/accuracy/accuracy.py\n",
+      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
+      "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
+      "  warnings.warn(\n",
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions = trainer.predict(tokenized_data['test'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "55887a6e-b1ec-4b9f-9f98-124ac2a8f647",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CommitInfo(commit_url='https://huggingface.co/Mahesh9/distil-bert-fintuned-issues-cfpb-complaints/commit/1b9049731d062501f46cc35fa1e303d421c9dd30', commit_message='Mahesh9/distil-bert-fintuned-issues-cfpb-complaints', commit_description='', oid='1b9049731d062501f46cc35fa1e303d421c9dd30', pr_url=None, pr_revision=None, pr_num=None)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer.push_to_hub(\"Mahesh9/distil-bert-fintuned-issues-cfpb-complaints\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "acf12dc2-39df-4736-abc1-93ba91652af5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predicted_categories = np.argmax(predictions.predictions, axis=-1)\n",
+    "true_categories = [label2id[label] for label in dataset[\"test\"][\"Issue\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "54064537-d77b-4e34-91ef-46cf067178c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jpvEs/ZfjSXTh4gXq+71Eo1fqMrD/+/z6yy8Wi+VJYh88YNXK5Tg5OeHlnXlZaE+qk4MH9uP3cnUCGvszYujHaX7J+KxZ+jl6/J0dHx/Pzh/DKFTYk+7vdKZu7Rq0f7MV25J0jzfe2dVTeGdb6GfxeTxHj58zK/38Z7V4EmWF/0NSi8H4/STJ/6vG7yfP4XcPyLr3Tf59LNOXUeQfYN68ebz11lsANGzYkNu3b7Nlyxbq168PwJgxY2jTpg0jRvxvDDdfX18ATp06xYoVK9i0aZNRvmjRoka5mTNn4uHhwYwZMzCZTPj4+HDp0iUGDRrE0KFDsbJK//cZgYGBtG3bFoCxY8cyffp09u/fT8OGDY1sgHz58qU5ic2pU6coWbJkitsS1586dcpYd//+fRYuXMgLL7wAwPTp02nSpAnBwcG4ubkxatQogoODjczbIkWKcPLkSebMmUPHjh2N4/Tp08csOxceNbol6tWrF6GhoaxcuZIXX3wRZ2dnsyy+1Hz66afUq1fPaFDz8vLi5MmTTJw40SzbtnHjxnTv3h141IA7efJkwsLC8PHx4eLFi5QoUYKXXnoJk8lE4cKFUz0fQN26dc0+z5kzBxcXF7Zv307Tpk2N9Wndr1mzZlG0aFEmT56MyWTC29vbaAh7VsqVK8ewYcMAKFGiBDNmzGDLli00aNAAwGy82CJFijBq1Cjee+89s0zhtDg6OmJnZ0dMTEyK96h///40adIEeNSAXLp0aU6fPo2Pj0+671vdunXNnpPHs3GdnZ2NP2BWr17N7Nmz2bx5M25ubpw5c4avvvqKX3/9lQIFChgxhYaGsmDBAsaOHcvUqVPx9/c3Gqy9vLzYvXu3WfZkRj148IDFixeTN29eY12LFi3MysybN498+fJx8uRJs/FZ06qzixcv0qJFC6MBMel7Jnv27GbvpyJFirB7925WrFhBq1aPxpwbPXo0/fr1o3fv3ka5KlWqABix5sqVK82ft0mTJjFo0CDatGkDwPjx49m2bRtTpkzhs88+S9d1PMnFixdxc3Ojfv36ZM+enUKFClG1alVjm4ODA02bNsXJyYnChQtToUKFJx4z0bZt2zh27BhXr17FxsbGuKY1a9bw9ddf061bNy5evMiAAQOMWEuUKJHu46dXQkICwRM+oULFShQv4QXAH3/8DmBkKCVydXU1up5d++N3smfPniwLxdXVlWvX/njmcT5u69bN/PXXX7zarHmmnyu9Phj8MSOGDeGVurXIli0bJpOJYSNHU7FS5Sfv/Iz8sP57IiJO8uXyr5/bOdNy488bxMXFJRtaw9U1j/Gc/ZfjAShbrhxjxo6nsKcn165dY+6cWXRo14bVa9eRK5eLRWJKyfawbQzq35f79++RJ29eZs+dj4tL5mTEPqlOar5ciwb+DXEvUIDffv2VmdOn0rVTR5atXE2OHDkyJaakLPkcJSQkEDzR/J19/fo17t69y4L5c+nRsze93+/Prp076Pd+Lz6ft5DKVaqm/c7+I/Pf2Yme53P0uKz285/V4oGs8X9IWjEk1ktKdXbpOXWNz4r3LVNpbFGLUc2LpCAyMpL9+/cbjQDZsmWjdevWzJ8/3ygTHh5OvXr1Utw/PDwca2trateuneL2iIgIqlevbpaVU7NmTW7fvs2vv/6aoVjLlStn/NvBwQEnJyeuXr2aoWOkR9JYCxUqZDSYAlSvXp34+HgiIyP5/fff+eWXX+jcuTOOjo7GMnr0aLOux4BZZi486k48ZswYypUrh6urK46OjmzcuDHD48lGRERQs6b55A01a9YkKirKbLyopHVnMplwc3Mz6i4wMJDw8HC8vb0JCgpi48aNaZ7z6tWrvPvuu3h5eRmNdrdv304We1r3KyIigmrVqpnVdfXq1TN07U+S9PwA7u7uZs/Ltm3baNCgAQULFsTJyYkOHTpw7do1Y3iGZ3l+d3d3ALPrT899e/y5Sc1PP/1Ehw4d+Oyzz3jppUffQh8+fJiEhAS8vLzMns/t27cbz2fiz2dSf/c+FC5c2KzBFODMmTO8+eabFC1alJw5c1KkSBGANJ+Zx+ssKCiI0aNHU7NmTYYNG8bRo0fN9p09ezaVK1cmb968ODo6MnfuXOP4V69e5dKlS6m+x9Lj1q1bXLp0KcX7FhERke7reJI33niDe/fuUbRoUbp27co333xjZKk2aNCAwoULU7RoUdq3b8/SpUu5m2R8uSc5dOgQt2/fNt45icu5c+eMZ6Jv37506dKF+vXr88knnyR7lyUVExPDrVu3zJaYmJgnxvHJmFFEnYpk3ITgZNsez+BMSEi+7nGPOkZkfubnN6tWUfOlWuTLlz/Tz5VeXy5dzNGj4UydMYuvVqyi34APGDtqBHv3ZM5wBY+7HB3NhE/GMPaTiUZDfFaR/FlKsGiGcFaK56WXa1P/FX9KeHlTrXoNps+cA8Da5zCue0ZUqfoiK1atYdHSZdR86WUG9OuTadmdT6qTho0aU6u2HyVKeOFXpy6fzZnLhfPn+XF7WKbEkxpLPEfGO3v8/97ZiT1F/Pzq8laHQLx9StKpSzderu3H1yuXpXm8hASeazfY5/kcpSYr/fxD1oknK/wfkt4YUq6zzI4uPTFkjZ4v8u+gRlORFMybN4+HDx9SsGBBsmXLRrZs2Zg1axarV6/mxo0bwKNxDFOT1jZI+WWe2PU/cb2VlZXZcACQ8gQyj3cRNZlMybr3PkliRl9KEhs+0sqsSow56bnnzp1LeHi4sRw/fpy9e/ea7efg4GD2OTg4mMmTJzNw4EC2bt1KeHg4/v7+PHjwIEPXk1b9JpVW3VWsWJFz584xatQo7t27R6tWrczGSXxcYGAghw4dYsqUKezevZvw8HBcXV2TxZ7WOVOKMT2cnJy4efNmsvV//vmnkXGZnvNfuHCBxo0bU6ZMGVatWsWhQ4eMTMHEZy+9z2Vqkp4/8R4lvf703LfHn5uUXL58mVdffZXOnTvTuXNnY318fDzW1tYcOnTI7PmMiIgwhq942vuQlpRiDggIeJS5M3cu+/btM4ahSOuZebzOunTpwtmzZ2nfvj3Hjh2jcuXKTJ8+HXg0VvH7779Pp06d2LhxI+Hh4bz99tvG8Z/0nsqI9PzCmtZ1PImHhweRkZF89tln2NnZ0b17d2rVqkVsbCxOTk4cPnyYr776Cnd3d4YOHYqvr68xxvGTntn4+Hjc3d3Nnofw8HAiIyMZMGAA8Gg81RMnTtCkSRO2bt1KqVKl+Oabb1KMddy4ccYXJ4nLpPGpj4cM8MnYUWzftpW58xeRP0lWb548jxraH88+un79mpF96ponL7Gxsdx67B1w/fq1TJ+w69Kl39i3dzevp/FufN7u37/PtCmT6T9wMH516uLl7UPbdm/h36gxCxfMe/IBnoGTJ09w/do12rZ6nYrlSlGxXCkOHtjPl0sXU7FcqUyboCstLrlcsLa25o8UniVX1zz/+XhSYm9vTwkvLy5ePG/pUMzY29tTqHBhyvmWZ8SosWSzzsaa1c8nG+1JdZI3bz4KFCjAxQspb3/WLPUcfTJ2FNvDtjJ3nvk728XFhWzZslG0WHGz8kWLFONydDRg2Xd2UpZ8jrLaz39Wiycr/B/ypBgS68WSdZbV7pv8e6nRVOQxDx8+ZNGiRQQHB5v9AX3kyBEKFy7M0qVLgUdZU1u2bEnxGGXLliU+Pp7t27enuL1UqVLs3r3b7A/53bt34+TkRMGCBYFHXWOj//8XLHiU0XXu3LkMXUti16gn/efapk0boqKi+O675LPEBgcH4+rqanTfhkeZcEm7XuzZswcrKyu8vLzInz8/BQsW5OzZsxQvXtxsScykS82OHTt47bXXeOutt/D19aVo0aLJxkXMkSPHE6+nVKlS7Ny502zd7t278fLywtraOs19k8qZMyetW7dm7ty5LF++nFWrVqU6s+iOHTsICgqicePGxqQ6j/8n/iSlSpVK1rD8+OeU+Pj4JBtvMiEhgUOHDuGdgfGpDh48yMOHDwkODqZatWp4eXkl62KTN29eLl++bPbsJo7RmSg99yglz+q+3b9/n9dee83o8p9UhQoViIuL4+rVq8mez8Qu6E97HzLi2rVrRERE8PHHH1OvXj1KlixpfCGTUR4eHrz77rusXr2afv36MXfuXODRM1mjRg26d+9OhQoVKF68uFmGpJOTE56enqm+x+BRQ2da9zJnzpwUKFAgxfuW2pAfT8vOzo5XX32VadOmERYWxp49ezh27BjwqDdA/fr1mTBhAkePHuX8+fNs3boVePK7tGLFily+fJls2bIleyby5PnfL91eXl68//77bNy4kddff50FCxakGOfgwYO5efOm2dJ/0OAUyyYkJPDJmJFs3byJOfNDKJgkgx+g4AsvkCdPXrMMydjYBxw6eADf8o+GIChZqjTZsmU3K/P771c5czoK3wwMU/A0vv1mNblzu/JyLb9MPU9GPHz4kIcPY7GyMm+0t7KyJj4TvhBJyYvVqvH1mu9YvmqNsZQuXYbGTQNYvmpNht5nz0r2HDkoWao0e3fvMlu/d/du41n6L8eTkgcPHnD27Bnjy4usKiEhIcNfLj+tJ9XJn3/e4PLlaPLmzfdc4nnez5Hxzt6yiTnzkr+zs2fPQanSZbhw3vz39QsXzuPu/mhIoDTf2RZ89p/nc5TVfv6zWjxZ4f+QJ8XwgofHo99PktRZ7IP///0kk3/3SJTV7lum00RQFqMxTUUes27dOm7cuEHnzp2TZem1bNmSefPm0bNnT4YNG0a9evUoVqwYbdq04eHDh/zwww8MHDgQT09POnbsSKdOnZg2bRq+vr5cuHCBq1ev0qpVK7p3786UKVPo1asXPXv2JDIykmHDhtG3b19jPNO6desSEhJizIw9ZMiQDP8nWbhwYUwmE+vWraNx48bY2dkZk8Qk1aZNG1auXEnHjh2ZOHEi9erV49atW3z22WesXbuWlStXmmXK2dra0rFjRyZNmsStW7cICgqiVatWRqPT8OHDCQoKImfOnDRq1IiYmBgOHjzIjRs36Nu3b6rxFi9enFWrVrF7925cXFz49NNPuXz5slnji6enJ/v27eP8+fM4OjqmOLNtv379qFKlCqNGjaJ169bs2bOHGTNmpHtcToDJkyfj7u5O+fLlsbKyYuXKlbi5uaU6Nmzx4sVZvHgxlStX5tatWwwYMCDDmXzvvvsuwcHB9O3bl3feeYdDhw4lmwQpJf3796djx474+PjwyiuvcO/ePT7//HPOnDljNkHXkxQrVoyHDx8yffp0AgIC2LVrF7NnzzYr4+fnx++//86ECRNo2bIloaGh/PDDD+TMmdMo4+npyYYNG4iMjMTV1TXZz1FqnsV9A3jnnXf45Zdf2LJlC7///r8xjXLnzo2Xlxft2rWjQ4cOBAcHU6FCBf744w+2bt1K2bJlady4MUFBQdSoUYMJEybQrFkzNm7c+LfGM02Ji4sLrq6ufP7557i7u3Px4sWnmvSrT58+NGrUCC8vL27cuMHWrVuNn5fixYuzaNEiNmzYQJEiRVi8eDEHDhww+/Ji+PDhvPvuu+TLl49GjRrx119/sWvXLnr16gVgNKrWrFkTGxsbXFySj+s3YMAAhg0bRrFixShfvjwLFiwgPDzc+ILpWQgJCSEuLo4XX3wRe3t7Fi9ejJ2dHYULF2bdunWcPXuWWrVq4eLiwvr164mPjze+MHjSu7R+/fpUr16dZs2aMX78eLy9vbl06RLr16+nWbNmlC5dmgEDBtCyZUuKFCnCr7/+yoEDB5KNSZvIxsYmWVe2u7EpN9aNGz2SH9avY/K0z3BwcDDG4HJ0dMLW1haTycSb7Tswb+4cChUqTKHChZk3dw62trY0avJorGQnJyeavd6CTyeOxzlXLpydnZk8aQLFS3jxYrXMmzwiPj6eb79ZTcBrzciW7fn+Onn3zh2zYSx++/VXfo6IwNnZGfcCBahcpSqfTpqIjY0t7gUKcOjAAdatXUP/gc92Yr3UODg4UuL/xzhMZGdvTy7nXMnWP0/tO77NRx8MpFSZMvj6VmDVyuVER0fzRus2igcInjie2n51cHN35/r168ydPYs7t28/1/F603q2nXPl4ovPZ+NXpy558ubl5p9/snzZl1y5cpkG/g0zJZ606uTunTvMmjmD+g1eIU/evFz67TemT51MLhcX6v7/eP7Pw/N8jsaN+f939tSU39kAHd/uzKD+falYqTKVq77I7p07+HH7NubOXwQkeWdPSvLODn627+ys9hylJKv9/GeleLLC/yHpiaFd4u8nhT0f/X7y+aPfTxo3aZrSITNFVrpv8u+lRlORx8ybN4/69eun2NDTokULxo4dy+HDh/Hz82PlypWMGjWKTz75hJw5c5rN2Dxr1iw+/PBDunfvzrVr1yhUqBAffvghAAULFmT9+vUMGDAAX19fcufOTefOnfn444+N/QcPHszZs2dp2rQpzs7OjBo1KsOZpgULFmTEiBF88MEHvP3223To0CHFRjiTycSKFSuYOnUqkydPpkePHtjY2FC9enW2bdtmjAeZqHjx4sYs79evX6dx48ZmDVtdunTB3t6eiRMnMnDgQBwcHChbtqzZJEMpGTJkCOfOncPf3x97e3u6detGs2bNzLqeJzYQlipVinv37qVYJxUrVmTFihUMHTqUUaNG4e7uzsiRI80mE3oSR0dHxo8fT1RUFNbW1lSpUoX169enOknX/Pnz6datGxUqVKBQoUKMHTvWbLKi9ChUqBCrVq3i/fffZ+bMmVStWpWxY8fSqVOnNPdr1aoVCQkJTJo0iY8++ghbW1sqVKjAjh07njiBVVLly5fn008/Zfz48QwePJhatWoxbtw4OnToYJQpWbIkM2fOZOzYsYwaNYoWLVrQv39/Pv/8c6NM165dCQsLo3Llyty+fZtt27bh6en5xPM/i/sGsH37dqKjoylVqpTZ+m3btuHn58eCBQuMCZB+++03XF1dqV69Oo0bNwagWrVqfPHFFwwbNozhw4dTv359Pv74Y0aNGpWhONJiZWXFsmXLCAoKokyZMnh7ezNt2jT8/PwydJy4uDh69OjBr7/+Ss6cOWnYsCGTJ08GHjXCh4eH07p1a0wmE23btqV79+788MMPxv4dO3bk/v37TJ48mf79+5MnTx6zYSgSG/Hnzp1LwYIFk026BY/GVb116xb9+vXj6tWrlCpVirVr1z7TyZJy5crFJ598Qt++fYmLi6Ns2bJ89913uLq6kitXLlavXs3w4cO5f/8+JUqU4KuvvqJ06dLAk9+lJpOJ9evX89FHH9GpUyd+//133NzcqFWrFvnz58fa2ppr167RoUMHrly5Qp48eXj99dfNJtl6WiuXfwVA17c7mK0fMXosrzZ7NEleYKcuxNy/z7jRI7l16yZlypVj1ufzcHD43xdg/QcNxjqbNYP69SEmJoaqL1Zj6oxZmZqNsnfPbqKjL9Hs9ZQbjzPTiRPH6ZKkziZNeDT8wauvNWfU2E8YP/FTpk75lMGD+nPr5k3cCxSgZ9D7vNG67XOPNStp2KgxN/+8weezZvL771cpXsKLz2Z/ToECBRUPcOXKZT4Y0JcbN/7EJbcL5cqVZ/GXK55rPGk92x8PG8G5c2dZ++03/HnjBrly5aJ0mbIsWLSU4sWf/eR0kHad3L9/n6hTp/hu7Rr+uvUXefPmpUrVF5kwabLZ+ymzPc/nyHhnd3rsnT3qf+/suvUa8NHQ4cz/4nMmfDKGwp5FmPjpNCpUrGSU7z9wMNbW1gzqnznv7Kz2HKUkq/38Z7V4/gne7tyVmJgYxo4awa1bNylbzpdZc+f/a3/+LU4TQVmMKSEzBm8TkX+t4cOHs2bNmmRdskVEJGWpZZpakpW6W4mIpCg+Pgu+s630zhbJbLZZOKXQrsaHFjv3vd1jLXburEDN1SIiIiIiIiIiIiJJZOG2dBERERERERERkf8w9RCyGHXPFxEREclE6p4vIvLPoe75Iv9NWbp7fs2PLHbue7vGWOzcWUEWfixERERERERERET+wzQRlMWo5kVERERERERERESSUKapiIiIiIiIiIhIVqRhlSxGmaYiIiIiIiIiIiIiSajRVERERERERERERCQJdc8XERERERERERHJijQRlMWo5kVERERERERERESSUKapiIiIiIiIiIhIVqRMU4tRzYuIiIiIiIiIiMhT+/HHHwkICKBAgQKYTCbWrFljtt1kMqW4TJw40Sjj5+eXbHubNm3MjnPjxg3at2+Ps7Mzzs7OtG/fnj///NOszMWLFwkICMDBwYE8efIQFBTEgwcPMnxNajQVERERERERERGRp3bnzh18fX2ZMWNGitujo6PNlvnz52MymWjRooVZua5du5qVmzNnjtn2N998k/DwcEJDQwkNDSU8PJz27dsb2+Pi4mjSpAl37txh586dLFu2jFWrVtGvX78MX5O654uIiIhkIiuTydIhJHM/Ns7SIZixzW5t6RBEMkVCgqUjMBef1QICrK2y1jvSKovFIyLCP+S91KhRIxo1apTqdjc3N7PP3377LXXq1KFo0aJm6+3t7ZOVTRQREUFoaCh79+7lxRdfBGDu3LlUr16dyMhIvL292bhxIydPnuSXX36hQIECAAQHBxMYGMiYMWPImTNnuq9JmaYiIiIiIiIiIiLyXFy5coXvv/+ezp07J9u2dOlS8uTJQ+nSpenfvz9//fWXsW3Pnj04OzsbDaYA1apVw9nZmd27dxtlypQpYzSYAvj7+xMTE8OhQ4cyFKcyTUVERERERERERLIiC04EFRMTQ0xMjNk6GxsbbGxs/tZxFy5ciJOTE6+//rrZ+nbt2lGkSBHc3Nw4fvw4gwcP5siRI2zatAmAy5cvky9fvmTHy5cvH5cvXzbK5M+f32y7i4sLOXLkMMqklzJNRURERERERERExMy4ceOMCZcSl3Hjxv3t486fP5927dpha2trtr5r167Ur1+fMmXK0KZNG77++ms2b97M4cOHjTKmFIa+SkhIMFufnjLpoUZTERERERERERERMTN48GBu3rxptgwePPhvHXPHjh1ERkbSpUuXJ5atWLEi2bNnJyoqCng0LuqVK1eSlfv999+N7FI3N7dkGaU3btwgNjY2WQbqk6jRVEREREREREREJCsymSy22NjYkDNnTrPl73bNnzdvHpUqVcLX1/eJZU+cOEFsbCzu7u4AVK9enZs3b7J//36jzL59+7h58yY1atQwyhw/fpzo6GijzMaNG7GxsaFSpUoZilVjmoqIiIiIiIiIiMhTu337NqdPnzY+nzt3jvDwcHLnzk2hQoUAuHXrFitXriQ4ODjZ/mfOnGHp0qU0btyYPHnycPLkSfr160eFChWoWbMmACVLlqRhw4Z07dqVOXPmANCtWzeaNm2Kt7c3AK+88gqlSpWiffv2TJw4kevXr9O/f3+6du1Kzpw5M3RNyjQVERERERERERHJikxWllsy4ODBg1SoUIEKFSoA0LdvXypUqMDQoUONMsuWLSMhIYG2bdsm2z9Hjhxs2bIFf39/vL29CQoK4pVXXmHz5s1YW1sb5ZYuXUrZsmV55ZVXeOWVVyhXrhyLFy82tltbW/P9999ja2tLzZo1adWqFc2aNWPSpEkZrXlMCQkJCRneS0RERETS5f5DS0eQ3P3YOEuHYMY2u/WTC4n8A2W1v7Tis1pAgLVVxiblEBHJDLZZuB+2Xf1PLHbue5s/sNi5s4Is/FiIiIiIiIiIiIj8h2Vwxnd5dtQ9X0RERERERERERCQJNZqKiIikw/nz5zGZTISHhwMQFhaGyWTizz//tGhcACEhIeTKlStD+5hMJtasWZMp8fzb+fn50adPH0uHISIiIiIimUiNpiIiWdTu3buxtramYcOGybYNHz6c8uXLJ1uf1RrCAgMDadasmcWP8U+QlRphIXkjcWbLas+uiIiIiEiW8A+ZCOrfSDUgIpJFzZ8/n169erFz504uXrxo6XBERERERERE/jPUaCoikgXduXOHFStW8N5779G0aVNCQkKMbSEhIYwYMYIjR45gMpkwmUyEhITg6ekJQPPmzTGZTMZngO+++45KlSpha2tL0aJFGTFiBA8f/m9Kb5PJxJw5c2jatCn29vaULFmSPXv2cPr0afz8/HBwcKB69eqcOXPG2Ccx23XOnDl4eHhgb2/PG2+8YWRKDh8+nIULF/Ltt98acYaFhfHgwQN69uyJu7s7tra2eHp6Mm7cuBTrIbVjABw7doy6detiZ2eHq6sr3bp14/bt22nW64kTJ2jSpAk5c+bEycmJl19+2bim+Ph4Ro4cyQsvvICNjQ3ly5cnNDQ0nXfskd27d1OrVi3s7Ozw8PAgKCiIO3fuGNtjYmIYOHAgHh4e2NjYUKJECebNm8f58+epU6cOAC4uLphMJgIDA1M9T0hICIUKFcLe3p7mzZtz7dq1ZGWedM8BoqOjadSoEXZ2dhQpUoSVK1ca24oUKQJAhQoVMJlM+Pn5pRhLYobsli1bqFy5Mvb29tSoUYPIyEizcrNmzaJYsWLkyJEDb29vFi9ebGxL69l9XFr3/dixY1hZWfHHH38AcOPGDaysrHjjjTeM/ceNG0f16tWNzydPnqRx48Y4OjqSP39+2rdvb+wPj34WO3TogKOjI+7u7gQHB6cam4iIiIjIM2cyWW75j1OjqYhIFrR8+XK8vb3x9vbmrbfeYsGCBSQkJADQunVr+vXrR+nSpYmOjiY6OprWrVtz4MABABYsWEB0dLTxecOGDbz11lsEBQVx8uRJ5syZQ0hICGPGjDE756hRo+jQoQPh4eH4+Pjw5ptv8s477zB48GAOHjwIQM+ePc32OX36NCtWrOC7774jNDSU8PBwevToAUD//v1p1aoVDRs2NOKsUaMG06ZNY+3ataxYsYLIyEiWLFmSaiNZase4e/cuDRs2xMXFhQMHDrBy5Uo2b96cLL6kfvvtN2rVqoWtrS1bt27l0KFDdOrUyWhInDp1KsHBwUyaNImjR4/i7+/Pq6++SlRUVLru2bFjx/D39+f111/n6NGjLF++nJ07d5rF1KFDB5YtW8a0adOIiIhg9uzZODo64uHhwapVqwCIjIwkOjqaqVOnpnieffv20alTJ7p37054eDh16tRh9OjRZmXSe8+HDBlCixYtOHLkCG+99RZt27YlIiICgP379wOwefNmoqOjWb16dZrX/9FHHxEcHMzBgwfJli0bnTp1MrZ988039O7dm379+nH8+HHeeecd3n77bbZt2waQ6rP7uCfd9zJlyuDq6sr27dsB+PHHH3F1deXHH380jhEWFkbt2rWBR43GtWvXpnz58hw8eJDQ0FCuXLlCq1atjPIDBgxg27ZtfPPNN2zcuJGwsDAOHTqUZl2IiIiIiMg/nykh8a9wERHJMmrWrEmrVq3o3bs3Dx8+xN3dna+++or69esDjzIw16xZk2y8SZPJxDfffGM2BmitWrVo1KgRgwcPNtYtWbKEgQMHcunSJWO/jz/+mFGjRgGwd+9eqlevzrx584zGr2XLlvH2229z7949I4bRo0dz/vx5XnjhBQBCQ0Np0qQJv/32G25ubgQGBvLnn3+ajVUZFBTEiRMn2Lx5M6Z0fHuZ0jHmzp3LoEGD+OWXX3BwcABg/fr1BAQEcOnSJfLnz5/sOB9++CHLli0jMjKS7NmzJ9tesGBBevTowYcffmisq1q1KlWqVOGzzz7j/PnzFClShJ9++ony5csTFhZGnTp1uHHjBrly5aJDhw7Y2dkxZ84cY/+dO3dSu3Zt7ty5w8WLF/H29mbTpk3GfUzq8eOl5s033+TGjRv88MMPxro2bdoQGhpqZPmm956/++67zJo1yyhTrVo1KlasyMyZM5Ndb2oS4968eTP16tUDHt2LJk2acO/ePWxtbalZsyalS5fm888/N/Zr1aoVd+7c4fvvvzfiefzZfVx67nuLFi0oUKAA06dP5/3338fa2pqFCxeyfft2vLy8cHFxYeXKlTRs2JChQ4eyb98+NmzYYJzj119/xcPDg8jISAoUKICrqyuLFi2idevWAFy/fp0XXniBbt26MWXKlFRjTer+wyeXed7ux8ZZOgQzttmtLR2CSKbIan9pxWe1gABrK2UyiYjl2WazdASps/OfZLFz39vQ32LnzgqUaSoiksVERkayf/9+2rRpA0C2bNlo3bo18+fPf6rjHTp0iJEjR+Lo6GgsXbt2JTo6mrt37xrlypUrZ/w7sdGxbNmyZuvu37/PrVu3jHWFChUyGkwBqlevTnx8fLKu2UkFBgYSHh6Ot7c3QUFBbNy4McPXFBERga+vr9FwBo8amtM6d3h4OC+//HKKDaa3bt3i0qVL1KxZ02x9zZo1jczLJzl06BAhISFm9ezv7098fDznzp0jPDwca2trI8vxaUVERJh1LweSfU7vPU/pOOm93sclfX7c3d0BuHr1qhHz36nbROm5735+fsYQDtu3b6dOnTrUqlWL7du3c+DAAe7du2fEcujQIbZt22ZWTz4+PgCcOXOGM2fO8ODBA7N6yp07N97e3qnGGBMTw61bt8yWmJiYDF2niIiIiIhBE0FZTBZuSxcR+W+aN28eDx8+pGDBgsa6hIQEsmfPzo0bN3BxccnQ8eLj4xkxYgSvv/56sm22trbGv5M2JiZmgKa0Lj4+PtVzJZZJK4O0YsWKnDt3jh9++IHNmzfTqlUr6tevz9dff53OK3pUH6mdI7X1dnZ2Tzzu4/umdZ7HxcfH88477xAUFJRsW6FChTh9+nS6jvMk6ekgkt57npL0Xu/jnvSs/J26Tc8+iev9/Pzo3bs3p0+f5vjx48a4tdu3b+fPP/+kUqVKODk5GfEFBAQwfvz4ZMdzd3dP99AMSY0bN44RI0aYrftoyDA+Hjo8w8cSERERERHLUbOxiEgW8vDhQxYtWkRwcDDh4eHGcuTIEQoXLszSpUsByJEjB3FxybvXZs+ePdn6ihUrEhkZSfHixZMtVlZ/77+BixcvGt29Afbs2YOVlRVeXl5pxpkzZ05at27N3LlzWb58OatWreL69espniOlY5QqVYrw8HCzSZZ27dpldu7HlStXjh07dhAbG5tiPAUKFGDnzp1m63fv3k3JkiVTuXpzFStW5MSJEynWc44cOShbtizx8fHGeJspXSeQYn0lVapUKfbu3Wu27vHP6b3nKR0nMdMyvfGkR8mSJZ9Ytyk9u49Lz31PHNd09OjR+Pr6kjNnTmrXrs327dvNxjOF/90zT0/PZPXk4OBA8eLFyZ49u1k93bhxg1OnTqUa4+DBg7l586bZMmDQ4FTLi4iIiIikSRNBWYwaTUVEspB169Zx48YNOnfuTJkyZcyWli1bMm/ePODRbOOJXb7/+OMPo/uvp6cnW7Zs4fLly9y4cQOAoUOHsmjRIoYPH86JEyeIiIhg+fLlfPzxx387XltbWzp27MiRI0fYsWMHQUFBtGrVCjc3NyOeo0ePEhkZyR9//EFsbCyTJ09m2bJl/Pzzz5w6dYqVK1fi5uaW6jieKR2jXbt2xrmPHz/Otm3b6NWrF+3bt09xPFN4NInVrVu3aNOmDQcPHiQqKorFixcb3boHDBjA+PHjWb58OZGRkXzwwQeEh4fTu3fvdNXFoEGD2LNnDz169CA8PJyoqCjWrl1Lr169jOvo2LEjnTp1Ys2aNZw7d46wsDBWrFgBQOHChTGZTKxbt47ff//dmBH+cUFBQYSGhjJhwgROnTrFjBkzCA0NNSuT3nu+cuVK5s+fz6lTpxg2bBj79+83JlXKly8fdnZ2xuRIN2/eTFc9pGTAgAGEhIQwe/ZsoqKi+PTTT1m9ejX9+/9vjKSUnt3Hpee+m0wmatWqxZIlS/Dz8wMeNZg/ePCALVu2GOsAevTowfXr12nbti379+/n7NmzbNy4kU6dOhEXF4ejoyOdO3dmwIABbNmyhePHjxMYGJjmlw02NjbkzJnTbLGxsXnquhMREREREctQo6mISBYyb9486tevj7Ozc7JtLVq0IDw8nMOHD9OiRQsaNmxInTp1yJs3L1999RUAwcHBbNq0CQ8PDypUqACAv78/69atY9OmTVSpUoVq1arx6aefUrhw4b8db/HixXn99ddp3Lgxr7zyCmXKlGHmzJnG9q5du+Lt7U3lypXJmzcvu3btwtHRkfHjx1O5cmWqVKnC+fPnWb9+faoNUSkdw97eng0bNnD9+nWqVKlCy5YtqVevHjNmzEg1VldXV7Zu3crt27epXbs2lSpVYu7cuUa38qCgIPr160e/fv0oW7YsoaGhrF27lhIlSqSrLsqVK8f27duJiori5ZdfpkKFCgwZMsQY3xNg1qxZtGzZku7du+Pj40PXrl2NrMmCBQsyYsQIPvjgA/Lnz280Xj6uWrVqfPHFF0yfPp3y5cuzcePGZI2h6b3nI0aMYNmyZZQrV46FCxeydOlSSpUqBTwaS3fatGnMmTOHAgUK8Nprr6WrHlLSrFkzpk6dysSJEyldujRz5sxhwYIFZg2YKT27j0vvfa9Tpw5xcXHG8U0mEy+//DIAL730klGuQIEC7Nq1i7i4OPz9/SlTpgy9e/fG2dnZeB4nTpxIrVq1ePXVV6lfvz4vvfQSlSpVeuq6EBERERHJEI1pajGmhPQMjiYiIvKY4cOHs2bNGsLDwy0dikiWdv+hpSNI7n7s3x924VmyzW5t6RBEMkVW+0srPqsFBFhbqfuniFiebRae8ceu8VSLnfve+vT1uvu3UrOxiIiIiIiIiIiISBJZuC1dRERERERERETkP0wTMlmMuueLiIiIZCJ1z38ydc+Xf6us9peWuueLiKQsS3fPbzLNYue+932Qxc6dFWThx0JEREREREREROQ/TBMyWYxqXkRERERERERERCQJNZqKiIiIiIiIiIiIJKHu+SIiIiIiIiIiIlmRuudbjGpeREREREREREREJAllmoqIiIiIiIiIiGRFJpOlI/jPUqapiIiIiIiIiIiISBLKNBUREREREREREcmKNKapxajmRURERERERERERJJQo6mIiIiIiIiIiIhIEuqeLyIiIvIfY5vd2tIhmImLT7B0CMlYW2nSBfn7strcHdZZLaAsKCHrvY6y3HMkIs+ZXgIWo0xTERERERERERERkSSUaSoiIiIiIiIiIpIVaSIoi1HNi4iIiIiIiIiIiCShRlMRERERERERERGRJNQ9X0REREREREREJCvSRFAWo0xTERERERERERERkSSUaSoiIiIiIiIiIpIFmZRpajHKNBURERERERERERFJQpmmIiIiIiIiIiIiWZAyTS1HmaYiIiIiIiIiIiIiSajRVERERERERERERCQJNZqKiIhkApPJxJo1a57JsYYPH0758uWfybFEREREROQfxGTB5T9OjaYiIiIZdPnyZXr16kXRokWxsbHBw8ODgIAAtmzZkinn69+/f6YdW9LvWTaEi4iIiIhI1qaJoERERDLg/Pnz1KxZk1y5cjFhwgTKlStHbGwsGzZsoEePHvz888/P/JyOjo44Ojo+8+OKiIiIiEjWpomgLEeZpiIiIhnQvXt3TCYT+/fvp2XLlnh5eVG6dGn69u3L3r17U93v2LFj1K1bFzs7O1xdXenWrRu3b982toeFhVG1alUcHBzIlSsXNWvW5MKFC0Dy7vmBgYE0a9aMSZMm4e7ujqurKz169CA2NtYoEx0dTZMmTbCzs6NIkSJ8+eWXeHp6MmXKlFRjPHDgAA0aNCBPnjw4OztTu3ZtDh8+bFbGZDLxxRdf0Lx5c+zt7SlRogRr165Ns86WLFlC5cqVcXJyws3NjTfffJOrV6+alTlx4gRNmjQhZ86cODk58fLLL3PmzBlj+/z58yldujQ2Nja4u7vTs2dPY9vFixd57bXXcHR0JGfOnLRq1YorV64kq6+k+vTpg5+fn/HZz8+PoKAgBg4cSO7cuXFzc2P48OHGdk9PTwCaN2+OyWQyPouIiIiIyL+TGk1FRETS6fr164SGhtKjRw8cHBySbc+VK1eK+929e5eGDRvi4uLCgQMHWLlyJZs3bzYa/h4+fEizZs2oXbs2R48eZc+ePXTr1i3Nb5W3bdvGmTNn2LZtGwsXLiQkJISQkBBje4cOHbh06RJhYWGsWrWKzz//PFlD5eP++usvOnbsyI4dO9i7dy8lSpSgcePG/PXXX2blRowYQatWrTh69CiNGzemXbt2XL9+PdXjPnjwgFGjRnHkyBHWrFnDuXPnCAwMNLb/9ttv1KpVC1tbW7Zu3cqhQ4fo1KkTDx8+BGDWrFn06NGDbt26cezYMdauXUvx4sUBSEhIoFmzZly/fp3t27ezadMmzpw5Q+vWrdO81pQsXLgQBwcH9u3bx4QJExg5ciSbNm0CHjUoAyxYsIDo6Gjjs4iIiIiI/Dupe76IiEg6nT59moSEBHx8fDK039KlS7l37x6LFi0yGltnzJhBQEAA48ePJ3v27Ny8eZOmTZtSrFgxAEqWLJnmMV1cXJgxYwbW1tb4+PjQpEkTtmzZQteuXfn555/ZvHkzBw4coHLlygB88cUXlChRIs1j1q1b1+zznDlzcHFxYfv27TRt2tRYHxgYSNu2bQEYO3Ys06dPZ//+/TRs2DDF43bq1Mn4d9GiRZk2bRpVq1bl9u3bODo68tlnn+Hs7MyyZcvInj07AF5eXsY+o0ePpl+/fvTu3dtYV6VKFQA2b97M0aNHOXfuHB4eHgAsXryY0qVLc+DAAaNcepQrV45hw4YBUKJECWbMmMGWLVto0KABefPmBR41jLu5uaV6jJiYGGJiYszWJVjbYGNjk+44REREREQSqXu+5SjTVEREJJ0SEhKAjP/iEhERga+vr1l2as2aNYmPjycyMpLcuXMTGBiIv78/AQEBTJ06lejo6DSPWbp0aaytrY3P7u7uRiZpZGQk2bJlo2LFisb24sWL4+LikuYxr169yrvvvouXlxfOzs44Oztz+/ZtLl68aFauXLlyxr8dHBxwcnJKM4v1p59+4rXXXqNw4cI4OTkZ3eITjxseHs7LL79sNJg+HtOlS5eoV69eiseOiIjAw8PDaDAFKFWqFLly5SIiIiLN631c0usC8zpNr3Hjxhl1l7hMHD8uQ8cQERERERHLU6OpiIhIOpUoUQKTyZThxriEhIRUG1oT1y9YsIA9e/ZQo0YNli9fjpeXV5pjpD7ewGgymYiPjzfOl1ocaQkMDOTQoUNMmTKF3bt3Ex4ejqurKw8ePEj3uR93584dXnnlFRwdHVmyZAkHDhzgm2++ATCOa2dnl2pMaW1LvKaU6jbpeisrq2TXnnT810QZua7UDB48mJs3b5otAwYNztAxREREREQSmUwmiy3/dWo0FRERSafcuXPj7+/PZ599xp07d5Jt//PPP1Pcr1SpUoSHh5vts2vXLqysrMy6oVeoUIHBgweze/duypQpw5dffvlUcfr4+PDw4UN++uknY93p06dTjS/Rjh07CAoKonHjxsakS3/88cdTxZDo559/5o8//uCTTz7h5ZdfxsfHJ1n2Zrly5dixY0eKDZlOTk54enqyZcuWFI9fqlQpLl68yC+//GKsO3nyJDdv3jSGOMibN2+yzN3w8PAMX0v27NmJi4tLs4yNjQ05c+Y0W9Q1X0RERETkn0eNpiIiIhkwc+ZM4uLiqFq1KqtWrSIqKoqIiAimTZtG9erVU9ynXbt22Nra0rFjR44fP862bdvo1asX7du3J3/+/Jw7d47BgwezZ88eLly4wMaNGzl16tQTxzVNjY+PD/Xr16dbt27s37+fn376iW7dumFnZ5fmN8bFixdn8eLFREREsG/fPtq1a/fETM8nKVSoEDly5GD69OmcPXuWtWvXMmrUKLMyPXv25NatW7Rp04aDBw8SFRXF4sWLiYyMBGD48OEEBwczbdo0oqKiOHz4MNOnTwegfv36lCtXjnbt2nH48GH2799Phw4dqF27tjGea926dTl48CCLFi0iKiqKYcOGcfz48QxfS2Lj7eXLl7lx48bfqhcRERERkfRQpqnlqNFUREQkA4oUKcLhw4epU6cO/fr1o0yZMjRo0IAtW7Ywa9asFPext7dnw4YNXL9+nSpVqtCyZUvq1avHjBkzjO0///wzLVq0wMvLi27dutGzZ0/eeeedp45z0aJF5M+fn1q1atG8eXO6du2Kk5MTtra2qe4zf/58bty4QYUKFWjfvj1BQUHky5fvqWOAR1meISEhrFy5klKlSvHJJ58wadIkszKurq5s3bqV27dvU7t2bSpVqsTcuXON7vIdO3ZkypQpzJw5k9KlS9O0aVOioqKAR79ErlmzBhcXF2rVqkX9+vUpWrQoy5cvN47v7+/PkCFDGDhwIFWqVOGvv/6iQ4cOGb6W4OBgNm3ahIeHBxUqVPgbtSIiIiIiIlmdKeFJA5yJiIjIP96vv/6Kh4cHmzdvTnVSJckc9x9aOoKsLy4+6/06am2l7AqR/6Ks+Nexkr1EMp9tNktHkDrntostdu6bX7W32Lmzgiz8WIiIiMjTSszcLFu2LNHR0QwcOBBPT09q1apl6dBERERERCS99MWJxajRVERE5F8oNjaWDz/8kLNnz+Lk5ESNGjVYunRpshniRUREREREJDk1moqIiPwL+fv74+/vb+kwRERERETkb9CETJajiaBEREREREREREREklCjqYiIiIiIiIiIiEgS6p4vIiIiIiIiIiKSBal7vuUo01REREREREREREQkCWWaioiIiIiIiIiIZEHKNLUcZZqKiIiIiIiIiIiIJKFMUxERERERERERkSxImaaWo0xTERERERERERERkSTUaCoiIiIiIiIiIiKShLrni4iIiIhFWVtlvW5nCQmWjsCceuaJPB/6WRORLEfvJYtRpqmIiIiIiIiIiIhIEso0FRERERERERERyYI0EZTlKNNUREREREREREREJAk1moqIiIiIiIiIiIgkoe75IiIiIiIiIiIiWZC651uOMk1FREREREREREREklCmqYiIiIiIiIiISBakTFPLUaapiIiIiIiIiIiISBJqNBUREREREREREZGn9uOPPxIQEECBAgUwmUysWbPGbHtgYCAmk8lsqVatmlmZmJgYevXqRZ48eXBwcODVV1/l119/NStz48YN2rdvj7OzM87OzrRv354///zTrMzFixcJCAjAwcGBPHnyEBQUxIMHDzJ8TWo0FRERERERERERyYpMFlwy4M6dO/j6+jJjxoxUyzRs2JDo6GhjWb9+vdn2Pn368M0337Bs2TJ27tzJ7du3adq0KXFxcUaZN998k/DwcEJDQwkNDSU8PJz27dsb2+Pi4mjSpAl37txh586dLFu2jFWrVtGvX7+MXRAa01RERERERERERET+hkaNGtGoUaM0y9jY2ODm5pbitps3bzJv3jwWL15M/fr1AViyZAkeHh5s3rwZf39/IiIiCA0NZe/evbz44osAzJ07l+rVqxMZGYm3tzcbN27k5MmT/PLLLxQoUACA4OBgAgMDGTNmDDlz5kz3NSnTVERE5AlS6l7ytIYPH0758uWfybFEREREROTf7fEu7c9ziYmJ4datW2ZLTEzMU19LWFgY+fLlw8vLi65du3L16lVj26FDh4iNjeWVV14x1hUoUIAyZcqwe/duAPbs2YOzs7PRYApQrVo1nJ2dzcqUKVPGaDAF8Pf3JyYmhkOHDmUoXjWaiojIf9rly5fp1asXRYsWxcbGBg8PDwICAtiyZUumnK9///6ZdmzJXOfPn8dkMhEeHm7pUEREREREMt24ceOMsUMTl3Hjxj3VsRo1asTSpUvZunUrwcHBHDhwgLp16xqNsJcvXyZHjhy4uLiY7Zc/f34uX75slMmXL1+yY+fLl8+sTP78+c22u7i4kCNHDqNMeql7voiI/GedP3+emjVrkitXLiZMmEC5cuWIjY1lw4YN9OjRg59//vmZn9PR0RFHR8dnftysIjY2luzZs1s6DBERERGRfwWTKYODiz5DgwcPpm/fvmbrbGxsnupYrVu3Nv5dpkwZKleuTOHChfn+++95/fXXU90vISHBrA5Sqo+nKZMeyjQVEZH/rO7du2Mymdi/fz8tW7bEy8uL0qVL07dvX/bu3ZvqfseOHaNu3brY2dnh6upKt27duH37trE9LCyMqlWr4uDgQK5cuahZsyYXLlwAknfPDwwMpFmzZkyaNAl3d3dcXV3p0aMHsbGxRpno6GiaNGmCnZ0dRYoU4csvv8TT05MpU6akGN+PP/5I9uzZk32T2q9fP2rVqmV8XrVqFaVLl8bGxgZPT0+Cg4PNyqc0LEGuXLkICQkB/pd5uWLFCvz8/LC1tWXJkiUpxvTpp59StmxZHBwc8PDwoHv37mZ1BrBr1y5q166Nvb09Li4u+Pv7c+PGDQDi4+MZP348xYsXx8bGhkKFCjFmzBhj3yfdEz8/P/r06WN2vmbNmhEYGGh89vT0ZOzYsXTq1AknJycKFSrE559/bmwvUqQIABUqVMBkMuHn55fitYqIiIiI/BvY2NiQM2dOs+VpG00f5+7uTuHChYmKigLAzc2NBw8eGL//J7p69aqROerm5saVK1eSHev33383K/P430E3btwgNjY2WQbqk6jRVERE/pOuX79OaGgoPXr0wMHBIdn2XLlypbjf3bt3adiwIS4uLhw4cICVK1eyefNmevbsCcDDhw9p1qwZtWvX5ujRo+zZs4du3bql+a3mtm3bOHPmDNu2bWPhwoWEhIQYDZMAHTp04NKlS4SFhbFq1So+//xzs/F/HlerVi2KFi3K4sWLjXUPHz5kyZIlvP3228CjMYNatWpFmzZtOHbsGMOHD2fIkCFm502vQYMGERQUREREBP7+/imWsbKyYtq0aRw/fpyFCxeydetWBg4caGwPDw+nXr16lC5dmj179rBz504CAgKMmTIHDx7M+PHjGTJkCCdPnuTLL780ful50j3JiODgYCpXrsxPP/1E9+7dee+994yM4/379wOwefNmoqOjWb16dYaPLyIiIiIicO3aNX755Rfc3d0BqFSpEtmzZ2fTpk1GmejoaI4fP06NGjUAqF69Ojdv3jR+LwfYt28fN2/eNCtz/PhxoqOjjTIbN27ExsaGSpUqZShGdc8XEZH/pNOnT5OQkICPj0+G9lu6dCn37t1j0aJFRmPrjBkzCAgIYPz48WTPnp2bN2/StGlTihUrBkDJkiXTPKaLiwszZszA2toaHx8fmjRpwpYtW+jatSs///wzmzdv5sCBA1SuXBmAL774ghIlSqR5zM6dO7NgwQIGDBgAwPfff8/du3dp1aoV8Cjzs169egwZMgQALy8vTp48ycSJE82yL9OjT58+aXapSSyTqEiRIowaNYr33nuPmTNnAjBhwgQqV65sfAYoXbo0AH/99RdTp05lxowZdOzYEYBixYrx0ksvAU++Jxn5Rrlx48Z0794deNQYPHnyZMLCwvDx8SFv3rwAuLq6pjrrZ0xMTLLB8ROsbZ7ZN/IiIiIi8t9iye75GXH79m1Onz5tfD537hzh4eHkzp2b3LlzM3z4cFq0aIG7uzvnz5/nww8/JE+ePDRv3hwAZ2dnOnfuTL9+/XB1dSV37tz079+fsmXLUr9+feDR31UNGzaka9euzJkzB4Bu3brRtGlTvL29AXjllVcoVaoU7du3Z+LEiVy/fp3+/fvTtWtXcubMmaFrUqapiIj8JyUkJAAZ/yUkIiICX19fs+zUmjVrEh8fT2RkJLlz5yYwMBB/f38CAgKYOnWq2becKSldujTW1tbGZ3d3dyOTNDIykmzZslGxYkVje/HixZMNkP64wMBATp8+bQwzMH/+fFq1amXEHRERQc2aNc32qVmzJlFRUUZ2Z3olNuamZdu2bTRo0ICCBQvi5OREhw4duHbtGnfu3AH+l2makoiICGJiYtLcntY9yYhy5coZ/zaZTLi5uaWZ1fu4lAbLnzj+6QbLFxERERH5pzh48CAVKlSgQoUKAPTt25cKFSowdOhQrK2tOXbsGK+99hpeXl507NgRLy8v9uzZg5OTk3GMyZMn06xZM1q1akXNmjWxt7fnu+++M/tbaenSpZQtW5ZXXnmFV155hXLlypn1sLO2tub777/H1taWmjVr0qpVK2M4tIxSpqmIiPwnlShRApPJREREBM2aNUv3fmkNIJ64fsGCBQQFBREaGsry5cv5+OOP2bRpE9WqVUtxv8cnTjKZTMTHxxvnSy2OtOTLl4+AgAAWLFhA0aJFWb9+PWFhYWlex+PHNJlMydYlHWs1UUrDGyR14cIFGjduzLvvvsuoUaPInTs3O3fupHPnzsbx7OzsUt0/rW2pXUvSa4BHwwOk51rSuhfpkdJg+QnWyjIVERERkafzT8k09fPzS/NvlA0bNjzxGLa2tkyfPp3p06enWiZ37typzqOQqFChQqxbt+6J53sSZZqKiMh/Uu7cufH39+ezzz4zsh2T+vPPP1Pcr1SpUoSHh5vts2vXLqysrPDy8jLWVahQgcGDB7N7927KlCnDl19++VRx+vj48PDhQ3766Sdj3enTp1ONL6kuXbqwbNky5syZQ7FixcwyS0uVKsXOnTvNyu/evRsvLy/jm9y8efOaZclGRUVx9+7dDF/DwYMHefjwIcHBwVSrVg0vLy8uXbpkVqZcuXJs2bIlxf1LlCiBnZ1dqtvTc08ev5a4uDiOHz+eoevIkSOHsW9qMnOwfBEREREReX7UaCoiIv9ZM2fOJC4ujqpVq7Jq1SqioqKIiIhg2rRpVK9ePcV92rVrh62tLR07duT48eNs27aNXr160b59e/Lnz8+5c+cYPHgwe/bs4cKFC2zcuJFTp049cVzT1Pj4+FC/fn26devG/v37+emnn+jWrRt2dnZP/NbZ398fZ2dnRo8ebUwAlahfv35s2bKFUaNGcerUKRYuXMiMGTPo37+/UaZu3brMmDGDw4cPc/DgQd59991kmZjpUaxYMR4+fMj06dM5e/YsixcvZvbs2WZlBg8ezIEDB+jevTtHjx7l559/ZtasWfzxxx/Y2toyaNAgBg4cyKJFizhz5gx79+5l3rx5wJPvSeK1fP/993z//ff8/PPPdO/ePV0Nz0nly5cPOzs7QkNDuXLlCjdv3sxwXYiIiIiIyD+DGk1FROQ/q0iRIhw+fJg6derQr18/ypQpQ4MGDdiyZQuzZs1KcR97e3s2bNjA9evXqVKlCi1btqRevXrMmDHD2P7zzz/TokULvLy86NatGz179uSdd9556jgXLVpE/vz5qVWrFs2bN6dr1644OTlha2ub5n5WVlYEBgYSFxdHhw4dzLZVrFiRFStWsGzZMsqUKcPQoUMZOXKk2SRQwcHBeHh4UKtWLd5880369++Pvb19huMvX748n376KePHj6dMmTIsXbqUcePMx/n08vJi48aNHDlyhKpVq1K9enW+/fZbsmV7NJLQkCFD6NevH0OHDqVkyZK0bt3aGGv0SfcEoFOnTnTs2JEOHTpQu3ZtihQpQp06dTJ0HdmyZWPatGnMmTOHAgUK8Nprr2W4LkREREREMsRkweU/zpTwpEHRREREJEv59ddf8fDwYPPmzalOjpSoa9euXLlyhbVr1z6n6ORx9x9aOgJ5GlntN+R/yHBmIiIi/0i2WXjGnwLvrrbYuS/Nft1i584KsvBjISIiIgBbt27l9u3blC1blujoaAYOHIinpye1atVKdZ+bN29y4MABli5dyrfffvscoxURERERkWflnzIR1L+RGk1FRESyuNjYWD788EPOnj2Lk5MTNWrUYOnSpWmOL/raa6+xf/9+3nnnHRo0aPAcoxUREREREfnnU/d8ERERkUyk7vn/TFntN2QlmYiIiGSerNw9/4Xuayx27l9nNrPYubMCTQQlIiIiIiIiIiIikoQaTUVERERERERERESSyMIJyCIiIiIiIiIiIv9dmgjKcpRpKiIiIiIiIiIiIpKEMk1FRERERERERESyIiWaWowyTUVERERERERERESSUKOpiIiIiIiIiIiISBLqni8iIiIiIiIiIpIFaSIoy1GmqYiIiIiIiIiIiEgSyjQVERERERERERHJgpRpajlqNBUREREReUxW+/skPj7B0iEkY2WVxSpJnigh6z1GWe5nTUREJJEaTUVERERERERERLIgZZpajsY0FREREREREREREUlCjaYiIiIiIiIiIiIiSah7voiIiIiIiIiISBak7vmWo0xTERERERERERERkSSUaSoiIiIiIiIiIpIVKdHUYpRpKiIiIiIiIiIiIpKEGk1FREREREREREREklD3fBERERERERERkSxIE0FZjjJNRURERERERERERJJQo6mIyL+QyWRizZo1AJw/fx6TyUR4eLhFY/o3CgkJIVeuXJYOIxlPT0+mTJmSKccOCwvDZDLx559/ZsrxRURERETkf0wmk8WW/zo1moqIPAeXL1+mV69eFC1aFBsbGzw8PAgICGDLli2Zfm4PDw+io6MpU6YMkP5Gr8RyiYurqyt169Zl165dmR5zegUGBtKsWTNLh5GmxPrbu3ev2fqYmBhcXV0xmUyEhYU903MeOHCAbt26mcWQ2Ij+d9WoUYPo6GicnZ2fyfFSo8ZZERERERGxJDWaiohksvPnz1OpUiW2bt3KhAkTOHbsGKGhodSpU4cePXqkul9sbOwzOb+1tTVubm5ky/Z0w1hHRkYSHR1NWFgYefPmpUmTJly9evWZxPa04uLiiI+Pt2gMGeHh4cGCBQvM1n3zzTc4Ojpmyvny5s2Lvb39Mz9ubGwsOXLkwM3NTd88i4iIiIg8ByaT5Zb/OjWaiohksu7du2Mymdi/fz8tW7bEy8uL0qVL07dvX7PsQ5PJxOzZs3nttddwcHBg9OjRAHz33XdUqlQJW1tbihYtyogRI3j48KGxX1RUFLVq1cLW1pZSpUqxadMms/Mn7Z5//vx56tSpA4CLiwsmk4nAwMA048+XLx9ubm6ULVuWjz/+mJs3b7Jv3z5j+8mTJ2ncuDGOjo7kz5+f9u3b88cffxjb/fz86NmzJz179iRXrly4urry8ccfk5CQYJS5ceMGHTp0wMXFBXt7exo1akRUVJSxPbEb/Lp16yhVqhQ2Nja8/fbbLFy4kG+//dbI5kwtYzM0NJSXXnrJOH/Tpk05c+ZMsjpavXo1derUwd7eHl9fX/bs2WN2nJCQEAoVKoS9vT3Nmzfn2rVradZdoo4dO7Js2TLu3btnrJs/fz4dO3ZMVvbYsWPUrVsXOzs7XF1d6datG7dv3za2J2bXTpo0CXd3d1xdXenRo4dZI3vS7vmenp4ANG/eHJPJZHwGmDVrFsWKFSNHjhx4e3uzePFis1hSeiYfzwBNvDcbNmygZMmSODo60rBhQ6Kjo43jPHz4kKCgIKP+Bw0aRMeOHTOUJXzhwgUCAgJwcXHBwcGB0qVLs379euDR89OuXTvy5s2LnZ0dJUqUMBqpU8pYDQ8Px2Qycf78eWPd7t27qVWrFnZ2dnh4eBAUFMSdO3eM7TNnzqREiRLY2tqSP39+WrZsme7YRURERETkn0eNpiIimej69euEhobSo0cPHBwckm1/fDzMYcOG8dprr3Hs2DE6derEhg0beOuttwgKCuLkyZPMmTOHkJAQxowZA0B8fDyvv/461tbW7N27l9mzZzNo0KBU4/Hw8GDVqlXA/zJIp06dmq5ruXv3rtEQlT17dgCio6OpXbs25cuX5+DBg4SGhnLlyhVatWpltu/ChQvJli0b+/btY9q0aUyePJkvvvjC2B4YGMjBgwdZu3Yte/bsISEhgcaNG5s1BN69e5dx48bxxRdfcOLECaZNm0arVq2MBrro6Ghq1KiRYux37tyhb9++HDhwgC1btmBlZUXz5s2TZat+9NFH9O/fn/DwcLy8vGjbtq3RQL1v3z46depE9+7dCQ8Pp06dOkbD9pNUqlSJIkWKGHX/yy+/8OOPP9K+fftkddywYUNcXFw4cOAAK1euZPPmzfTs2dOs3LZt2zhz5gzbtm1j4cKFhISEEBISkuK5Dxw4AMCCBQuIjo42Pn/zzTf07t2bfv36cfz4cd555x3efvtttm3bZrb/489kSu7evcukSZNYvHgxP/74IxcvXqR///7G9vHjx7N06VIWLFjArl27uHXrVoaHC+jRowcxMTH8+OOPHDt2jPHjxxuZukOGDOHkyZP88MMPREREMGvWLPLkyZPuYx87dgx/f39ef/11jh49yvLly9m5c6dR7wcPHiQoKIiRI0cSGRlJaGgotWrVylD8IiIiIiLyz/J0fTVFRCRdTp8+TUJCAj4+Pukq/+abb5o1TLVv354PPvjAyEgsWrQoo0aNYuDAgQwbNozNmzcTERHB+fPneeGFFwAYO3YsjRo1SvH41tbW5M6dG3iUQZqeSYwSj3v37l0SEhKoVKkS9erVAx5lKlasWJGxY8ca5efPn4+HhwenTp3Cy8sLeNRYO3nyZEwmE97e3hw7dozJkyfTtWtXoqKiWLt2Lbt27TIaPZcuXYqHhwdr1qzhjTfeAB51DZ85cya+vr7Guezs7IiJicHNzS3Na2jRooXZ53nz5pEvXz5OnjxpjPUK0L9/f5o0aQLAiBEjKF26NKdPn8bHx4epU6fi7+/PBx98AICXlxe7d+8mNDT0iXUI8PbbbzN//nzeeustFixYQOPGjcmbN69ZmaVLl3Lv3j0WLVpkNLLPmDGDgIAAxo8fT/78+YFHWcIzZszA2toaHx8fmjRpwpYtW+jatWuy8yaeI1euXGb1NGnSJAIDA+nevTuAkfk8adIkIxsZkj+T586dS3aO2NhYZs+eTbFixQDo2bMnI0eONLZPnz6dwYMH07x5c+OaErNE0+vixYu0aNGCsmXLAo9+FpJuq1ChApUrVwYwy6ZNj4kTJ/Lmm2/Sp08fAEqUKMG0adOoXbs2s2bN4uLFizg4ONC0aVOcnJwoXLgwFSpUyNA5RERERESehobFshxlmoqIZKLELujp/Y8usdEn0aFDhxg5ciSOjo7G0rVrV6Kjo7l79y4REREUKlTIaNgEqF69+rO7AGDHjh0cPnyYr776isKFCxMSEmJkmh46dIht27aZxZfYQJy0+3u1atXM6qB69epERUURFxdHREQE2bJl48UXXzS2u7q64u3tTUREhLEuR44clCtX7qmu4cyZM7z55psULVqUnDlzUqRIEeBRY1tSSY/v7u4OYIzfGhERkaxuM1LXb731Fnv27OHs2bOEhISkmLUZERGBr6+vWVZyzZo1iY+PJzIy0lhXunRprK2tzWLN6DizERER1KxZ02xdzZo1zeockj+TKbG3tzcaTB+P5+bNm1y5coWqVasa262tralUqVKG4g0KCmL06NHUrFmTYcOGcfToUWPbe++9x7JlyyhfvjwDBw5k9+7dGTr2oUOHCAkJMXuO/f39iY+P59y5czRo0IDChQtTtGhR2rdvz9KlS7l7926Kx4qJieHWrVtmS0xMTIbiERERERERy1OjqYhIJipRogQmkylZQ1RqHu/CHx8fz4gRIwgPDzeWY8eOERUVha2trdm4oIme9TeRRYoUwcvLi9atWzNixAiaN29uNALFx8cTEBBgFl94eLgxzmp6pHQNieuTXoudnd1TX1tAQADXrl1j7ty57Nu3zxiT9cGDB2blEhuD4X/1mNiFP7U40ytxLNXOnTtz//79FLOBH7/mpJKuTxpn4ranmRjr8XOldP6UhpV4XErxPF5fKZ0rI7p06cLZs2dp3749x44do3LlykyfPh2ARo0aceHCBfr06cOlS5eoV6+eMTyAlZVVsvM9PslafHw877zzjtkzfOTIEaKioihWrBhOTk7GFwfu7u4MHToUX19fs3FSE40bNw5nZ2ezZeL4cRm6VhERERGRRJoIynLUaCoikoly586Nv78/n332mdmkMolSanRJqmLFikRGRlK8ePFki5WVFaVKleLixYtcunTJ2OfxyYselyNHDuDRDPQZ1b59e+Lj45k5c6YR34kTJ/D09EwWX9LGtqQTXiV+LlGiBNbW1pQqVYqHDx+aTS517do1Tp06RcmSJZ94LU+6jmvXrhEREcHHH39MvXr1KFmyJDdu3MjopVOqVKkUryMjOnXqRFhYGB06dDDLFE16jvDwcLNnZdeuXVhZWRlDHTyN7NmzJ6unkiVLsnPnTrN1u3fvfmKdZ5SzszP58+dn//79xrq4uDh++umnDB/Lw8ODd999l9WrV9OvXz/mzp1rbMubNy+BgYEsWbKEKVOm8PnnnxvrAbOJqcLDw82Om/gcp/Rzlvjzki1bNurXr8+ECRM4evQo58+fZ+vWrcliHDx4MDdv3jRbBgwanOFrFRERERERy1KjqYhIJps5cyZxcXFUrVqVVatWERUVRUREBNOmTXti9+6hQ4eyaNEihg8fzokTJ4iIiGD58uV8/PHHANSvXx9vb286dOjAkSNH2LFjBx999FGaxyxcuDAmk4l169bx+++/m83M/iRWVlb06dOHTz75hLt379KjRw+uX79O27Zt2b9/P2fPnmXjxo106tTJrJHul19+oW/fvkRGRvLVV18xffp0evfuDTzKxn3ttdfo2rUrO3fu5MiRI7z11lsULFiQ1157Lc14PD09OXr0KJGRkfzxxx/JMgjh0fifrq6ufP7555w+fZqtW7fSt2/fdF9zoqCgIEJDQ5kwYQKnTp1ixowZ6R7PNFHDhg35/fffzcb7TKpdu3bY2trSsWNHjh8/zrZt2+jVqxft27c3xjN9Gp6enmzZsoXLly8bDcYDBgwgJCSE2bNnExUVxaeffsrq1avNJnB6Vnr16sW4ceP49ttviYyMpHfv3ty4cSNDmcN9+vRhw4YNnDt3jsOHD7N161ajgXfo0KF8++23nD59mhMnTrBu3TpjW/HixfHw8GD48OGcOnWK77//nuDgYLNjDxo0iD179tCjRw8jU3rt2rX06tULgHXr1jFt2jTCw8O5cOECixYtIj4+Hm9v72Rx2tjYkDNnTrPFxsbmaatOREREREQsRI2mIiKZrEiRIhw+fJg6derQr18/ypQpQ4MGDdiyZQuzZs1Kc19/f3/WrVvHpk2bqFKlCtWqVePTTz+lcOHCwKNGzG+++YaYmBiqVq1Kly5dGDNmTJrHLFiwICNGjOCDDz4gf/78yWZmf5JOnToRGxvLjBkzKFCgALt27SIuLg5/f3/KlClD7969cXZ2NrpFA3To0IF79+5RtWpVevToQa9evejWrZuxfcGCBVSqVImmTZtSvXp1EhISWL9+fbJu34/r2rUr3t7eVK5cmbx587Jr165kZaysrFi2bBmHDh2iTJkyvP/++0ycODFD1wyPxmX94osvmD59OuXLl2fjxo1G43V6mUwm8uTJY2QvPs7e3p4NGzZw/fp1qlSpQsuWLalXrx4zZszIcLxJBQcHs2nTJjw8PIwJjJo1a8bUqVOZOHEipUuXZs6cOSxYsAA/P7+/da6UDBo0iLZt29KhQweqV69ujBlqa2ub7mPExcXRo0cPSpYsScOGDfH29jYynnPkyMHgwYMpV64ctWrVwtrammXLlgGPsmy/+uorfv75Z3x9fRk/fjyjR482O3a5cuXYvn07UVFRvPzyy1SoUIEhQ4YY49rmypWL1atXU7duXUqWLMns2bP56quvKF269DOqIRERERGRlJlMJost/3WmhL87SJuIiEga/Pz8KF++PFOmTLF0KJJFxMfHU7JkSVq1asWoUaMsHU6mu//Q0hHIv0F8fNb7ld3KSn9M/dNkxb/89De5iGQFttksHUHqvAdtsNi5I8f7W+zcWUEWfixERETk3+DChQts3LiR2rVrExMTw4wZMzh37hxvvvmmpUMTEREREcnS9OWS5ah7voiIiGQqKysrQkJCqFKlCjVr1uTYsWNs3rz5mU86JSIiIiIi8qwo01RERDJVWFiYpUMQC/Pw8EhxvFkREREREUmbhsOxHGWaioiIiIiIiIiIiCShRlMRERERERERERGRJNQ9X0REREREREREJAvSRFCWo0xTERERERERERERkSSUaSoiIiIiIiIiIpIFmZRqajHKNBURERERERERERFJQo2mIiIiIiIiIiIiIkmoe76IiIiIiIiIiEgWpN75lqNMUxEREREREREREZEklGkqIiIiIiIiIiKSBWkiKMtRo6mIiIiISBZnZZX1/mCKi0+wdAhmrLNgHWU1+rtbREQk/dRoKiIiIiIiIiIikgUp09RyNKapiIiIiIiIiIiISBJqNBURERERERERERFJQt3zRUREREREREREsiD1zrccZZqKiIiIiIiIiIiIJKFMUxERERERERERkSxIE0FZjjJNRURERERERERERJJQo6mIiIiIiIiIiIhIEuqeLyIiIiIiIiIikgWpd77lKNNUREREREREREREJAk1moqI/It4enoyZcoU47PJZGLNmjUWiyclfn5+9OnTJ80yISEh5MqV67nEk1kCAwNp1qxZpp9n+PDhlC9fPtPP80+UnmdNRERERCQrM5lMFlv+69Q9X0QkEwUGBrJw4UIAsmXLRu7cuSlXrhxt27YlMDAQK6vM/e4qOjoaFxeXTD1HRq1evZrs2bMbnz09PenTp48at+SZe/xZExERERERSS9lmoqIZLKGDRsSHR3N+fPn+eGHH6hTpw69e/emadOmPHz4MFPP7ebmho2NTaaeI6Ny586Nk5OTpcN4Zh48eGDpEP622NjYLHWcJ0lvnWf2s/ZvuPciIiIiIpIyNZqKiGQyGxsb3NzcKFiwIBUrVuTDDz/k22+/5YcffiAkJMQod/PmTbp160a+fPnImTMndevW5ciRI8b2M2fO8Nprr5E/f34cHR2pUqUKmzdvTvPcSbvnnz9/HpPJxOrVq6lTpw729vb4+vqyZ88es33mzp2Lh4cH9vb2NG/enE8//TTNrvItWrSgV69exuc+ffpgMpk4ceIEAA8fPsTJyYkNGzYA5l2m/fz8uHDhAu+//36KXUA2bNhAyZIlcXR0NBqfU5KQkEDx4sWZNGmS2frjx49jZWXFmTNnALh48SKvvfYajo6O5MyZk1atWnHlyhWjfEpd6vv06YOfn5/x2c/Pj549e9K3b1/y5MlDgwYNUq2bx2OcMGECRYsWxc7ODl9fX77++mtje1hYGCaTiS1btlC5cmXs7e2pUaMGkZGRZsf55JNPyJ8/P05OTnTu3Jn79+8nO9eCBQsoWbIktra2+Pj4MHPmTGNb4nOwYsUK/Pz8sLW1ZcmSJSnG/KT6ShwaYP78+RQtWhQbGxsSEhKSHefChQsEBATg4uKCg4MDpUuXZv369cb2kydP0rhxYxwdHcmfPz/t27fnjz/+MLanVOdt27alTZs2ZueJjY0lT548LFiwwNgvaQZzTEwMAwcOxMPDAxsbG0qUKMG8efP+VhwiIiIiIpnJZLLc8l+nRlMREQuoW7cuvr6+rF69GnjUoNakSRMuX77M+vXrOXToEBUrVqRevXpcv34dgNu3b9O4cWM2b97MTz/9hL+/PwEBAVy8eDFD5/7oo4/o378/4eHheHl50bZtWyPjddeuXbz77rv07t2b8PBwGjRowJgxY9I8np+fH2FhYcbn7du3kydPHrZv3w7AgQMHuH//PjVr1ky27+rVq3nhhRcYOXIk0dHRZo2id+/eZdKkSSxevJgff/yRixcv0r9//xRjMJlMdOrUyWgsSzR//nxefvllihUrRkJCAs2aNeP69ets376dTZs2cebMGVq3bp2uektq4cKFZMuWjV27djFnzpx07fPxxx+zYMECZs2axYkTJ3j//fd56623jHpK9NFHHxEcHMzBgwfJli0bnTp1MratWLGCYcOGMWbMGA4ePIi7u7tZgyg8avT+6KOPGDNmDBEREYwdO5YhQ4YYw0QkGjRoEEFBQURERODv758s3vTW1+nTp1mxYgWrVq0iPDw8xWvv0aMHMTEx/Pjjjxw7dozx48fj6OgIPBpConbt2pQvX56DBw8SGhrKlStXaNWqldkxHq/zdu3asXbtWm7fvm2U2bBhA3fu3KFFixYpxtGhQweWLVvGtGnTiIiIYPbs2X87DhERERER+XfSmKYiIhbi4+PD0aNHAdi2bRvHjh3j6tWrRnf6SZMmsWbNGr7++mu6deuGr68vvr6+xv6jR4/mm2++Ye3atfTs2TPd5+3fvz9NmjQBYMSIEZQuXZrTp0/j4+PD9OnTadSokdE46eXlxe7du1m3bl2qx/Pz86N379788ccfWFtbc+LECYYNG0ZYWBjdu3cnLCyMSpUqGY1TSeXOnRtra2ucnJxwc3Mz2xYbG8vs2bMpVqwYAD179mTkyJGpxvH2228zdOhQ9u/fT9WqVYmNjWXJkiVMnDgRgM2bN3P06FHOnTuHh4cHAIsXL6Z06dIcOHCAKlWqpLsOixcvzoQJE9Jd/s6dO3z66ads3bqV6tWrA1C0aFF27tzJnDlzqF27tlF2zJgxxucPPviAJk2acP/+fWxtbZkyZQqdOnWiS5cuwKNnYPPmzWbZpqNGjSI4OJjXX38dgCJFinDy5EnmzJlDx44djXJ9+vQxyqQkvfX14MEDFi9eTN68eVM91sWLF2nRogVly5Y1rj3RrFmzqFixImPHjjXWzZ8/Hw8PD06dOoWXlxeQvM6LFSuGg4MD33zzDe3btwfgyy+/JCAggJw5cyaL4dSpU6xYsYJNmzZRv379ZxaHiIiIiEhm0oRMlqNMUxERC0lISDD+Azx06BC3b9/G1dUVR0dHYzl37pzRtfzOnTsMHDiQUqVKkStXLhwdHfn5558znGlarlw549/u7u4AXL16FYDIyEiqVq1qVv7xz48rU6YMrq6ubN++nR07duDr68urr75qZFCGhYWZNQqml729vdFgmhhrYpwpcXd3p0mTJsyfPx+AdevWcf/+fd544w0AIiIi8PDwMBoAAaMuIyIiMhRb5cqVM1T+5MmT3L9/nwYNGpjd30WLFhn3N1Fa9yciIsJodE2U9PPvv//OL7/8QufOnc3OM3r06GTnedI1pLe+ChcunGaDKUBQUBCjR4+mZs2aDBs2zPiyAB49+9u2bTOL18fHB8As5sfjzZ49O2+88QZLly4FHv18fPvtt7Rr1y7FGMLDw7G2tk71WXzaOB4XExPDrVu3zJaYmJg09xERERERkaxHjaYiIhYSERFBkSJFAIiPj8fd3Z3w8HCzJTIykgEDBgAwYMAAVq1axZgxY9ixYwfh4eGULVs2w5PRJJ1NPLHRNj4+HjBvyE2U0hiVSZlMJmrVqkVYWBjbt2/Hz8+PMmXKEBcXx7Fjx9i9e7fZmKBPE2fieZ4US5cuXVi2bBn37t1jwYIFtG7dGnt7+1Sv7fH1VlZWyc6R0uRGDg4OGbqWxPr9/vvvze7vyZMnzcY1hbTvT3rPM3fuXLPzHD9+nL1792boGtJTX+k5Djy6L2fPnqV9+/YcO3aMypUrM336dCPmgICAZM9+VFQUtWrVSvM87dq1Y/PmzVy9epU1a9Zga2tLo0aNUozBzs4uzRj/ThxJjRs3DmdnZ7Nl4vhxae4jIiIiIpKaf8qYpj/++CMBAQEUKFDAbG4NePQ31aBBgyhbtiwODg4UKFCADh06cOnSJbNj+Pn5GXNdJC6Pz2Nw48YN2rdvb/yu3b59e/7880+zMhcvXiQgIAAHBwfy5MlDUFDQU03iqu75IiIWsHXrVo4dO8b7778PQMWKFbl8+TLZsmXD09MzxX127NhBYGAgzZs3Bx6NcXr+/PlnGpePjw/79+83W3fw4MEn7ufn58fnn39Ojhw5GDlyJCaTiZdffplJkyZx7969FMczTZQjRw7i4uL+duwAjRs3xsHBgVmzZvHDDz/w448/GttKlSrFxYsX+eWXX4zsyZMnT3Lz5k1KliwJQN68eTl+/LjZMcPDw5M14GZUqVKlsLGx4eLFi0+VdZuoZMmS7N27lw4dOhjrkjaG5s+fn4IFC3L27NlUMy4zEvOT6isjPDw8ePfdd3n33XcZPHgwc+fOpVevXlSsWJFVq1bh6elJtmwZ+7WkRo0aeHh4sHz5cn744QfeeOMNcuTIkWLZsmXLEh8fz/bt243u+Un9nTiSGjx4MH379jVbl2Bt89THExERERH5J7hz5w6+vr68/fbbyeYYuHv3LocPH2bIkCH4+vpy48YN+vTpw6uvvprs782uXbuaDcv2ePLDm2++ya+//kpoaCgA3bp1o3379nz33XcAxMXF0aRJE/LmzcvOnTu5du0aHTt2JCEhwUjcSC81moqIZLKYmBguX75MXFwcV65cITQ0lHHjxtG0aVOj8at+/fpUr16dZs2aMX78eLy9vbl06RLr16+nWbNmVK5cmeLFi7N69WoCAgIwmUwMGTIk3RmI6dWrVy9q1arFp59+SkBAAFu3buWHH3544jg6ieOaZsuWjZdfftlY169fPypWrJjiGJOJPD09+fHHH2nTpg02NjbkyZPnqeO3trYmMDCQwYMHU7x4cbOu6/Xr16dcuXK0a9eOKVOm8PDhQ7p3707t2rWNLtd169Zl4sSJLFq0iOrVq7NkyRKOHz9OhQoVnjomACcnJ/r378/7779PfHw8L730Erdu3WL37t04OjqajTWalt69e9OxY0cqV67MSy+9xNKlSzlx4oTZ2JzDhw8nKCiInDlz0qhRI2JiYjh48CA3btxI1piXlvTUV3r16dOHRo0a4eXlxY0bN9i6davR8NqjRw/mzp1L27ZtGTBgAHny5OH06dMsW7aMuXPnYm1tnepxTSYTb775JrNnz+bUqVNs27Yt1bKenp507NiRTp06MW3aNHx9fblw4QJXr16lVatWfyuOpGxsbIxxiRPdf5iuXUVERERE/rEaNWqUaq8vZ2dnNm3aZLZu+vTpVK1alYsXL1KoUCFjvb29fbL5LhJFREQQGhrK3r17efHFF4FHveyqV69OZGQk3t7ebNy4kZMnT/LLL79QoEABAIKDgwkMDGTMmDFp/m36OHXPFxHJZKGhobi7u+Pp6UnDhg3Ztm0b06ZN49tvvzUaYkwmE+vXr6dWrVp06tQJLy8v2rRpw/nz58mfPz8AkydPxsXFhRo1ahAQEIC/vz8VK1Z8prHWrFmT2bNn8+mnn+Lr60toaCjvv/8+tra2ae5XpkwZ8uTJg6+vr/GfUO3atYmLi3tiZuXIkSM5f/48xYoVe+LYmOnRuXNnHjx4YDbrPGB0EXFxcaFWrVrUr1+fokWLsnz5cqOMv78/Q4YMYeDAgVSpUoW//vrLLKvz7xg1ahRDhw5l3LhxlCxZEn9/f7777jtjiIb0aN26NUOHDmXQoEFUqlSJCxcu8N5775mV6dKlC1988QUhISGULVuW2rVrExISkqHzQPrqK73i4uLo0aMHJUuWpGHDhnh7ezNz5kwAChQowK5du4iLi8Pf358yZcrQu3dvnJ2dsbJ68q8p7dq14+TJkxQsWDDNjGZ4NNlTy5Yt6d69Oz4+PnTt2pU7d+48kzhERERERDLD493Vn+eSmeP137x5E5PJRK5cuczWL126lDx58lC6dGn69+/PX3/9ZWzbs2cPzs7ORoMpQLVq1XB2dmb37t1GmTJlyhgNpvDo77yYmBgOHTqUoRhNCU8aIE5ERP7Tunbtys8//8yOHTssHUq67Nq1Cz8/P3799VejwVnEkpRpKv9WcfFZ688IayvNLiwiIk/HNgv3w35x3HaLnbtRzDZGjBhhtm7YsGEMHz48zf1MJhPffPMNzZo1S3H7/fv3eemll/Dx8WHJkiXG+rlz51KkSBHc3Nw4fvy40YMwMUt17NixhISEcOrUKbPjeXl58fbbbzN48GC6devG+fPn2bhxo1kZGxsbQkJCaNu2bTqvXt3zRUTkMZMmTaJBgwY4ODjwww8/sHDhQiMrMCuLiYnhl19+YciQIbRq1UoNpiIiIiIi8o+X0QmZnqWUxut/fCiqjIqNjaVNmzbEx8cn+zuza9euxr/LlClDiRIlqFy5MocPHzZ6WaZnstr0lEkP9TcTEREz+/fvp0GDBpQtW5bZs2czbdo0unTpYumwnuirr77C29ubmzdvMmHCBEuHIyIiIiIi8o9mY2NDzpw5zZa/02gaGxtLq1atOHfuHJs2bXri+KIVK1Yke/bsREVFAeDm5saVK1eSlfv999+NpBk3NzcuX75stv3GjRvExsZmOLFGmaYiImJmxYoVlg7hqQQGBhIYGGjpMEREREREROQxiQ2mUVFRbNu2DVdX1yfuc+LECWJjY3F3dwegevXq3Lx5k/3791O1alUA9u3bx82bN6lRo4ZRZsyYMURHRxv7bdy4ERsbGypVqpShmNVoKiIiIiIiIiIikgVltEu5pdy+fZvTp08bn8+dO0d4eDi5c+emQIECtGzZksOHD7Nu3Tri4uKMbNDcuXOTI0cOzpw5w9KlS2ncuDF58uTh5MmT9OvXjwoVKhgTviZOLNu1a1fmzJkDQLdu3WjatCne3t4AvPLKK5QqVYr27dszceJErl+/Tv/+/enatesTM1sfp4mgRERERDKRJoKSfytNBCUiIv8WWXkiqOrjf7TYufcMqpXusmFhYdSpUyfZ+o4dOzJ8+HCKFCmS4n7btm3Dz8+PX375hbfeeovjx49z+/ZtPDw8aNKkCcOGDSN37txG+evXrxMUFMTatWsBePXVV5kxYwa5cuUyyly8eJHu3buzdetW7OzsePPNN5k0aVKGhxZQo6mIiIhIJlKjqfxbqdFURET+LbJyo2mNCZZrNN09MP2Npv9GmghKREREREREREREJIks3JYuIiIiIiIiIiLy3/VPGdP030iZpiIiIiIiIiIiIiJJqNFUREREREREREREJAl1zxcREREREREREcmC1DvfcpRpKiIiIiIiIiIiIpKEMk1FRERERERERESyIE0EZTlqNBURERH5j0lIsHQE5vS3wD+TtVXWunHxWe3BBqz0cIuIiPxjqXu+iIiIiIiIiIiISBLKNBUREREREREREcmC1D3fcpRpKiIiIiIiIiIiIpKEMk1FRERERERERESyICWaWo4yTUVERERERERERESSUKapiIiIiIiIiIhIFqQxTS1HmaYiIiIiIiIiIiIiSajRVERERERERERERCQJdc8XERERERERERHJgtQ733KUaSoiIiIiIiIiIiKShDJNRUREREREREREsiBNBGU5yjQVEfmXM5lMrFmzxtJh/GMMHz6c8uXLP7PjhYWFYTKZ+PPPP5/ZMSV1ISEh5MqVy9JhiIiIiIjIP5waTUVE/oECAwMxmUyYTCayZ89O/vz5adCgAfPnzyc+Pt6sbHR0NI0aNXpusf3bG63Onz+PyWQiW7Zs/Pbbb2bboqOjyZYtGyaTifPnzwNQo0YNoqOjcXZ2tkC0/wxP27Dv6enJlClTzNa1bt2aU6dOPZvARERERETkP0uNpiIi/1ANGzYkOjqa8+fP88MPP1CnTh169+5N06ZNefjwoVHOzc0NGxsbC0b671SgQAEWLVpktm7hwoUULFjQbF2OHDlwc3N7qm41CQkJZvfyWYqLi0vWwP5vYGdnR758+SwdhoiIiIjIM2EyWW75r1OjqYjIP5SNjQ1ubm4ULFiQihUr8uGHH/Ltt9/yww8/EBISYpRLmsX34MEDevbsibu7O7a2tnh6ejJu3DizsrNmzaJRo0bY2dlRpEgRVq5caWxPqat5eHi4kVkZFhbG22+/zc2bN41M2OHDhxvnHjhwIAULFsTBwYEXX3yRsLAw4zgXLlwgICAAFxcXHBwcKF26NOvXr0/1+pcsWULlypVxcnLCzc2NN998k6tXryaLdcuWLVSuXBl7e3tq1KhBZGSk2XE++eQT8ufPj5OTE507d+b+/fvpqv+OHTuyYMECs3UhISF07NjRbN3jdZbWdSaW3bBhA5UrV8bGxoYdO3aQkJDAhAkTKFq0KHZ2dvj6+vL1118nO8f333+Pr68vtra2vPjiixw7dswstly5crFu3TpKlSqFjY0NFy5c+Nv35eTJkzRu3BhHR0fy589P+/bt+eOPP4ztfn5+BAUFMXDgQHLnzo2bm5vxTMCjbFGA5s2bYzKZjM9nzpzhtddeI3/+/Dg6OlKlShU2b95sdtwLFy7w/vvvG89a0utMatasWRQrVowcOXLg7e3N4sWLzbabTCa++OILmjdvjr29PSVKlGDt2rXG9hs3btCuXTvy5s2LnZ0dJUqUSHbvRURERETk30WNpiIi/yJ169bF19eX1atXp7h92rRprF27lhUrVhAZGcmSJUuMRqpEQ4YMoUWLFhw5coS33nqLtm3bEhERka7z16hRgylTppAzZ06io6OJjo6mf//+ALz99tvs2rWLZcuWcfToUd544w0aNmxIVFQUAD169CAmJoYff/yRY8eOMX78eBwdHVM914MHDxg1ahRHjhxhzZo1nDt3jsDAwGTlPvroI4KDgzl48CDZsmWjU6dOxrYVK1YwbNgwxowZw8GDB3F3d2fmzJnputZXX32VGzdusHPnTgB27tzJ9evXCQgISHO/9FznwIEDGTduHBEREZQrV46PP/6YBQsWMGvWLE6cOMH777/PW2+9xfbt2832GzBgAJMmTeLAgQPky5ePV199ldjYWGP73bt3GTduHF988QUnTpwgX758f+u+REdHU7t2bcqXL8/BgwcJDQ3lypUrtGrVyiyuhQsX4uDgwL59+5gwYQIjR45k06ZNABw4cACABQsWEB0dbXy+ffs2jRs3ZvPmzfz000/4+/sTEBDAxYsXAVi9ejUvvPACI0eONJ61lHzzzTf07t2bfv36cfz4cd555x3efvtttm3bZlZuxIgRtGrViqNHj9K4cWPatWvH9evXgUc/EydPnuSHH34gIiKCWbNmkSdPnjTvs4iIiIjIs2BlMlls+a/LZukARETk2fLx8eHo0aMpbrt48SIlSpTgpZdewmQyUbhw4WRl3njjDbp06QLAqFGj2LRpE9OnT09XY2KOHDlwdnbGZDLh5uZmrD9z5gxfffUVv/76KwUKFACgf//+hIaGsmDBAsaOHcvFixdp0aIFZcv+H3v3Hddl9f9//PEG2UtEFFQMTUER9wotVyrONCvITZplpmbO/FSKK/dILfVjCqSWaY7MzC2m4kANR+LIj6YWZu5ciMDvD39cX94yBEdvzOe923W7wXWd65xzDUhe79c5pzwAJUuWzLat9MHPkiVLMnXqVGrUqMG1a9fMgpCjRo2ibt26AHzwwQc0b96cW7duYW9vz5QpU+jSpYtxvSNHjmT9+vU5yja1sbGhQ4cOzJ07l+eff565c+fSoUMHbGxssj0vJ9c5fPhwGjVqBMD169eZNGkSGzduJCgoyDhn69atzJo1y7g2gKFDhxrnRUVFUaxYMZYtW2YEMZOSkvj888+pWLEi8PDPZcaMGVSpUoVPPvnE2Dd37lx8fHw4evQofn5+AFSoUIGhQ4cCULp0aaZPn86GDRto1KgRnp6eAOTPn9/snalYsaLRT7j7bJYtW8aKFSvo2bMnBQoUwNra2sg0zsqECRMICwujR48eAPTt25cdO3YwYcIE6tevb5QLCwujbdu2AHzyySdMmzaNXbt20aRJE06dOkXlypWpVq0aQIYPGtJLTEwkMTHRbF+qtZ2myBARERERecIo01RE5F8mNTU1y/kzw8LCiIuLw9/fn969e7N27doMZdICc+m/z2mmaVb27t1Lamoqfn5+ODs7G9vmzZs5fvw4AL1792bkyJHUrl2boUOHZhn4TfPzzz/TqlUrnnnmGVxcXKhXrx6AkYmYpkKFCsbX3t7eAMYw/vj4+EyvN6e6du3K4sWLOXv2LIsXLzYL5GYlJ9eZFpyDu8Pfb926RaNGjczu3Zdffmncu8z6XqBAAfz9/c2ena2trdn9eNjnsmfPHjZt2mR2bpkyZQDM+pa+Tbj7HNJPpZCZ69evM3DgQAICAsifPz/Ozs4cPnw4w/O9n/j4eGrXrm22r3bt2hne6fR9dHJywsXFxejjO++8w8KFC6lUqRIDBw4kJiYmy/ZGjx6Nm5ub2TZ+7Ogsy4uIiIiIZEdzmlqOMk1FRP5l4uPjKVGiRKbHqlSpwokTJ/jxxx9Zv349ISEhNGzY0Gx+zMykBWGtrO5+1paammocSz/8OyspKSlYW1uzZ88erK2tzY6lZYW++eabBAcH88MPP7B27VpGjx7NxIkT6dWrV4b6rl+/TuPGjWncuDHz58/H09OTU6dOERwczO3bt83Kps/8TLuOR7UAUmBgIGXKlKFt27aULVuWwMBA4uLisj0nJ9fp5ORkfJ3W1x9++CHDIlM5yV5MH0B3cHAw+/5hn0tKSgotW7Zk7NixGdpNC1ADGbJvTSbTfZ/BgAEDWLNmDRMmTKBUqVI4ODjw6quvZni+OXHvhwiZfbCQXR+bNm3Kb7/9xg8//MD69et58cUXeffdd5kwYUKGtgYPHkzfvn3N27NWlqmIiIiIyJNGmaYiIv8iGzdu5MCBA7zyyitZlnF1dSU0NJTZs2fzzTffsGTJEmPuRoAdO3aYld+xY4eRPZg2lDr9/JH3BgltbW1JTk4221e5cmWSk5M5d+4cpUqVMtvSD6328fGhe/fuLF26lH79+jF79uxMr+Hw4cOcP3+eMWPG8MILL1CmTJn7Zi5mpmzZspleb2506dKF6OjoHGWZpsnpdQLGok2nTp3KcO98fHyy7PulS5c4evSo8ewy87DPpUqVKvzyyy/4+vpmOD994Pd+bGxsMrwzW7ZsISwsjJdffpny5cvj5eXFyZMnzcpk9q7dq2zZssa8s2liYmIoW7ZsjvsHd9/9sLAw5s+fz5QpU/jvf/+baTk7OztcXV3NNg3NFxERERF58ijTVETkCZWYmMjZs2dJTk7mzz//ZPXq1YwePZoWLVrQqVOnTM+ZPHky3t7eVKpUCSsrKxYvXoyXl5fZauOLFy+mWrVqPP/88yxYsIBdu3YxZ84cACNQFx4ezsiRIzl27BgTJ040a8PX15dr166xYcMGKlasiKOjI35+frRv355OnToxceJEKleuzPnz59m4cSPly5enWbNm9OnTh6ZNm+Ln58elS5fYuHFjloGt4sWLY2try7Rp0+jevTsHDx5kxIgRub6H7733Hp07dza73l9++eW+86mm161bN1577bUMK7ZnJTfXCeDi4kL//v15//33SUlJ4fnnn+fq1avExMTg7OxM586djbLDhw/Hw8ODwoUL8+GHH1KwYEFat26dZd0P+1zeffddZs+eTdu2bRkwYAAFCxbk119/ZeHChcyePTtD9mpWfH192bBhA7Vr18bOzg53d3dKlSrF0qVLadmyJSaTiY8//jhDdqqvry8//fQTr7/+OnZ2dpkuzjRgwABCQkKoUqUKL774It9//z1Lly5l/fr1OeobwJAhQ6hatSrlypUjMTGRlStX5jroKiIiIiLyILKaek0eP2Waiog8oVavXo23tze+vr40adKETZs2MXXqVL777rssg1XOzs6MHTuWatWqUb16dU6ePMmqVauMYfdwdxXxhQsXUqFCBaKioliwYAEBAQHA3YzAr7/+msOHD1OxYkXGjh3LyJEjzdqoVasW3bt3JzQ0FE9PT8aNGwfcXR29U6dO9OvXD39/f1566SV27txpZEsmJyfz7rvvUrZsWZo0aYK/v3+Wi095enoSGRnJ4sWLCQgIYMyYMZkOlb6f0NBQhgwZwqBBg6hatSq//fYb77zzTq7qyJcvHwULFiRfvpx9Dpmb60wzYsQIhgwZwujRoylbtizBwcF8//33GaZhGDNmDO+99x5Vq1YlISGBFStWYGtrm23dD/NcihQpwrZt20hOTiY4OJjAwEDee+893NzczN6p+5k4cSLr1q3Dx8eHypUrA3cD/O7u7tSqVYuWLVsSHBxMlSpVzM4bPnw4J0+e5NlnnzWyoO/VunVrPv30U8aPH0+5cuWYNWsWERERxhy4OWFra8vgwYOpUKECderUwdramoULF+b4fBERERERefKYUtNPTCciIk81k8nEsmXLss1OlLwnOjqa+vXrc+nSpRxnvMo/59YdS/cgo7z2rz8lUMijkJLXXmzASi+3iMgTwT4Pj8NuOmOnxdr+8Z2aFms7L1CmqYiIiIiIiIiIiEg6CpqKiIiIiIiIiIiIpJOHE5BFROSfphlbnkz16tXTsxMRERER+RfSQlCWo0xTERERERERERERkXSUaSoiIiIiIiIiIpIHKdHUcpRpKiIiIiIiIiIiIpKOMk1FRERERERERETyIBNKNbUUZZqKiIiIiIiIiIiIpKOgqYiIiIiIiIiIiEg6Gp4vIiIiIiIiIiKSB1lpdL7FKNNUREREREREREREJB1lmoqIiIiIiIiIiORBJpNSTS1FQVMRERGRp4z+7S3/RlZ58MWuMmStpbtgZvPgBpbuQgYuDvqTVB5eaqqle2DuTkqKpbtgxsZag4xFHoR+ckRERERERERERETS0cd6IiIiIiIiIiIieVAeHEjx1FCmqYiIiIiIiIiIiEg6yjQVERERERERERHJg/LinN1PC2WaioiIiIiIiIiIiKSjTFMREREREREREZE8SImmlqNMUxEREREREREREZF0FDQVERERERERERERSUfD80VERERERERERPIgk8bnW4wyTUVERERERERERETSUaapiIiIiIiIiIhIHqREU8tRpqmIiMhTIDw8nMKFC2MymVi+fLmluyMiIiIiIpKnKWgqIiIPJSwsjNatW1u6G5KN+Ph4hg0bxqxZs0hISKBp06aW7tITKTw8nEqVKlm6GyIiIiIi8g/Q8HwREXkiJScnYzKZsLKyzOd/SUlJ2NjYWKTt3Dp+/DgArVq1ynMTyd++fRtbW9tHVu5xSE1NJTk52SJti4iIiMjTzSqP/fv9aaJMUxEReaTq1atHr1696NOnD+7u7hQuXJj//ve/XL9+nTfeeAMXFxeeffZZfvzxR+Oc6OhoTCYTP/zwAxUrVsTe3p6aNWty4MABo0xkZCT58+dn5cqVBAQEYGdnx2+//calS5fo1KkT7u7uODo60rRpU44dO5bhvOXLl+Pn54e9vT2NGjXi9OnTZv3+/vvvqVq1Kvb29pQsWZJhw4Zx584d47jJZGLmzJm0atUKJycnRo4cmen1Zzb8PX/+/ERGRgJ3g389e/bE29sbe3t7fH19GT16tFH2ypUrvPXWWxQqVAhXV1caNGjAvn37sr3nBw4coEGDBjg4OODh4cFbb73FtWvXgLvZkS1btgTAysoq06BpamoqpUqVYsKECWb7Dx48iJWVlRF0PXXqFK1atcLZ2RlXV1dCQkL4888/jfKZZR336dOHevXqGd/Xq1ePnj170rdvXwoWLEijRo0yvaa0ukaPHk2RIkXw8/MD4Pfffyc0NBR3d3c8PDxo1aoVJ0+ezHDesGHDjHv49ttvc/v2baNMYmIivXv3plChQtjb2/P8888TGxtrHE97H9esWUO1atWws7Nj3rx5DBs2jH379mEymTCZTMYzFRERERGRfx8FTUVE5JGLioqiYMGC7Nq1i169evHOO+/w2muvUatWLfbu3UtwcDAdO3bkxo0bZucNGDCACRMmEBsbS6FChXjppZdISkoyjt+4cYPRo0fzxRdf8Msvv1CoUCHCwsLYvXs3K1asYPv27aSmptKsWbMM540aNYqoqCi2bdvG1atXef31143ja9asoUOHDvTu3ZtDhw4xa9YsIiMjGTVqlFn/hg4dSqtWrThw4ABdunR5oHszdepUVqxYwaJFizhy5Ajz58/H19cXuBu8bN68OWfPnmXVqlXs2bOHKlWq8OKLL3Lx4sVM67tx4wZNmjTB3d2d2NhYFi9ezPr16+nZsycA/fv3JyIiAoCEhAQSEhIy1GEymejSpYtRLs3cuXN54YUXePbZZ0lNTaV169ZcvHiRzZs3s27dOo4fP05oaGiu70FUVBT58uVj27ZtzJo1K8tyGzZsID4+nnXr1rFy5Upu3LhB/fr1cXZ25qeffmLr1q04OzvTpEkTs6Bo2nmbNm3i66+/ZtmyZQwbNsw4PnDgQJYsWUJUVBR79+6lVKlSBAcHZ7jHAwcOZPTo0cTHx9O4cWP69etHuXLljPv4INcuIiIiIpIbJgtuTzsNzxcRkUeuYsWKfPTRRwAMHjyYMWPGULBgQbp16wbAkCFDmDFjBvv37+e5554zzhs6dKiReRgVFUWxYsVYtmwZISEhwN0h8Z9//jkVK1YE4NixY6xYsYJt27ZRq1YtABYsWICPjw/Lly/ntddeM86bPn06NWvWNOouW7Ysu3btokaNGowaNYoPPviAzp07A1CyZElGjBjBwIEDGTp0qNG/du3aPXCwNM2pU6coXbo0zz//PCaTiWeeecY4tmnTJg4cOMC5c+ews7MDYMKECSxfvpxvv/2Wt956K0N9CxYs4ObNm3z55Zc4OTkBMH36dFq2bMnYsWMpXLgw+fPnB8DLyyvLfr3xxhsMGTLEuCdJSUnMnz+f8ePHA7B+/Xr279/PiRMn8PHxAWDevHmUK1eO2NhYqlevnuN7UKpUKcaNG3ffck5OTnzxxRfGsPy5c+diZWXFF198YWTMRkREkD9/fqKjo2ncuDEAtra2zJ07F0dHR8qVK8fw4cMZMGAAI0aM4ObNm8yYMYPIyEhjbtfZs2ezbt065syZw4ABA4z2hw8fbpYJ6+zsTL58+bK9j4mJiSQmJprtS7W2M56niIiIiIg8GZRpKiIij1yFChWMr62trfHw8KB8+fLGvsKFCwNw7tw5s/OCgoKMrwsUKIC/vz/x8fHGPltbW7O64+PjyZcvnxEMBfDw8MhwXr58+ahWrZrxfZkyZcifP79RZs+ePQwfPhxnZ2dj69atGwkJCWbZsOnreFBhYWHExcXh7+9P7969Wbt2rXFsz549XLt2DQ8PD7O+nDhxwhgif6/4+HgqVqxoBEwBateuTUpKCkeOHMlxv7y9vWnevDlz584FYOXKldy6dcsIPMfHx+Pj42METAECAgLM7mNO5fQ+li9f3mwe0z179vDrr7/i4uJi3JsCBQpw69Yts/tTsWJFHB0dje+DgoK4du0ap0+f5vjx4yQlJVG7dm3juI2NDTVq1MhwHQ/yvEePHo2bm5vZNn7s6PufKCIiIiIieYoyTUVE5JG7d4Ekk8lkti8tSzAlJeW+daWfg9PBwcHs+9TU1EzPSU1NzTB3Z2Zzeabvx7Bhw2jTpk2GMvb29sbX6QOT2fX33n6lnyqgSpUqnDhxgh9//JH169cTEhJCw4YN+fbbb0lJScHb25vo6OgM9aZli94rs2tN35fcePPNN+nYsSOTJ08mIiKC0NBQI/iYVTvp91tZWWV77Wlych8zK5eSkkLVqlVZsGBBhrKenp73rS/9s7n3WjK7vpz2M73BgwfTt29f87qtlWUqIiIiIg8mry3k+jRR0FRERPKMHTt2ULx4cQAuXbrE0aNHKVOmTJblAwICuHPnDjt37jSG51+4cIGjR49StmxZo9ydO3fYvXs3NWrUAODIkSNcvnzZqLtKlSocOXKEUqVKPfQ1eHp6ms0beuzYsQxzt7q6uhIaGkpoaCivvvoqTZo04eLFi1SpUoWzZ8+SL18+Y57T+wkICCAqKorr168bQb5t27ZhZWVlLJ6UU82aNcPJyYkZM2bw448/8tNPP5m1c+rUKU6fPm1kmx46dIgrV64Y99rT05ODBw+a1RkXF5chiP6gqlSpwjfffGMs8JSVffv2cfPmTRwcHIC775WzszPFihXDw8MDW1tbtm7dSrt27YC7gd3du3fTp0+fbNu3tbUlOTk52zJ2dhmH4t+6k0VhERERERHJszQ8X0RE8ozhw4ezYcMGDh48SFhYGAULFsywGnt6pUuXplWrVnTr1o2tW7eyb98+OnToQNGiRWnVqpVRzsbGhl69erFz50727t3LG2+8wXPPPWcEUYcMGcKXX35JeHg4v/zyC/Hx8XzzzTfGvKy50aBBA6ZPn87evXvZvXs33bt3NwsaTp48mYULF3L48GGOHj3K4sWL8fLyIn/+/DRs2JCgoCBat27NmjVrOHnyJDExMXz00Ufs3r070/bat2+Pvb09nTt35uDBg2zatIlevXrRsWNHYxqEnLK2tiYsLIzBgwdTqlQps+kSGjZsSIUKFWjfvj179+5l165ddOrUibp16xrD2Bs0aMDu3bv58ssvOXbsGEOHDs0QRH0Y7du3p2DBgrRq1YotW7Zw4sQJNm/ezHvvvceZM2eMcrdv36Zr164cOnSIH3/8kaFDh9KzZ0+srKxwcnLinXfeYcCAAaxevZpDhw7RrVs3bty4QdeuXbNt39fXlxMnThAXF8f58+czzF0qIiIiIvKoWZkstz3tFDQVEZE8Y8yYMbz33ntUrVqVhIQEVqxYYTanZWYiIiKoWrUqLVq0ICgoiNTUVFatWmUWqHR0dGTQoEG0a9eOoKAgHBwcWLhwoXE8ODiYlStXsm7dOqpXr85zzz3HpEmTzBZpyqmJEyfi4+NDnTp1aNeuHf379zebX9PZ2ZmxY8dSrVo1qlevzsmTJ1m1ahVWVlaYTCZWrVpFnTp16NKlC35+frz++uucPHkyywCoo6Mja9as4eLFi1SvXp1XX32VF198kenTp+e67wBdu3bl9u3bGRa8MplMLF++HHd3d+rUqUPDhg0pWbIk33zzjVEmODiYjz/+mIEDB1K9enX+/vtvOnXq9ED9yIyjoyM//fQTxYsXp02bNpQtW5YuXbpw8+ZNs8zTF198kdKlS1OnTh1CQkJo2bIl4eHhxvExY8bwyiuv0LFjR6pUqcKvv/7KmjVrcHd3z7b9V155hSZNmlC/fn08PT35+uuvH9m1iYiIiIhI3mJKzWpCOBERkX9IdHQ09evX59KlS1nO3fmgIiMj6dOnD5cvX36k9f5bbdu2jXr16nHmzJlcZ6rmBWFhYVy+fJnly5dbuisGDc8XeXpVGbL2/oX+QZsHN7B0FzJwcdCMcfLw8lpU404O5u3/J9lYK1/ufuzz8K+iDvP3Wazt+R0qWqztvCAPvxYiIiLyT0lMTOT06dN8/PHHhISEPJEBUxERERERkUdFHzeIiIgIX3/9Nf7+/ly5coVx48ZZujsiIiIiIiIWpeH5IiIiIo+RhueLPL00PP/+NDxfHoW8FtXQ8PwnT14ent9xgeWG589r/3QPz9dPjoiIiIiIiIiIiEg6eTiWLiIiIiIiIiIi8vQymUyW7sJTS5mmIiIiIiIiIiIi8sB++uknWrZsSZEiRTCZTCxfvtzseGpqKuHh4RQpUgQHBwfq1avHL7/8YlYmMTGRXr16UbBgQZycnHjppZc4c+aMWZlLly7RsWNH3NzccHNzo2PHjly+fNmszKlTp2jZsiVOTk4ULFiQ3r17c/v27Vxfk4KmIiIiIiIiIiIi8sCuX79OxYoVmT59eqbHx40bx6RJk5g+fTqxsbF4eXnRqFEj/v77b6NMnz59WLZsGQsXLmTr1q1cu3aNFi1akJycbJRp164dcXFxrF69mtWrVxMXF0fHjh2N48nJyTRv3pzr16+zdetWFi5cyJIlS+jXr1+ur0kLQYmIiIg8RloISuTppYWg7k8LQcmjkNeiGloI6smTlxeCCvt6v8Xajmxb4YHOM5lMLFu2jNatWwN3s0yLFClCnz59GDRoEHA3q7Rw4cKMHTuWt99+mytXruDp6cm8efMIDQ0F4I8//sDHx4dVq1YRHBxMfHw8AQEB7Nixg5o1awKwY8cOgoKCOHz4MP7+/vz444+0aNGC06dPU6RIEQAWLlxIWFgY586dw9XVNcfXoZ8cERERERERERERMZOYmMjVq1fNtsTExFzXc+LECc6ePUvjxo2NfXZ2dtStW5eYmBgA9uzZQ1JSklmZIkWKEBgYaJTZvn07bm5uRsAU4LnnnsPNzc2sTGBgoBEwBQgODiYxMZE9e/bkqt8KmoqIiIiIiIiIiORBJpPJYtvo0aONuUPTttGjR+f6Gs6ePQtA4cKFzfYXLlzYOHb27FlsbW1xd3fPtkyhQoUy1F+oUCGzMve24+7ujq2trVEmp/JwArKIiIiIiIiIiIhYwuDBg+nbt6/ZPjs7uweuz2QymX2fmpqaYd+97i2TWfkHKZMTyjQVERERERERERHJg0wW3Ozs7HB1dTXbHiRo6uXlBZAh0/PcuXNGVqiXlxe3b9/m0qVL2Zb5888/M9T/119/mZW5t51Lly6RlJSUIQP1fnKUabpixYocV/jSSy/lqgMiIiIi8s9KyWMrZljl8lN/kSfFpg/qW7oLZorX6WPpLmRwKTbzVZZFciOv/X8tMUkLQYmkV6JECby8vFi3bh2VK1cG4Pbt22zevJmxY8cCULVqVWxsbFi3bh0hISEAJCQkcPDgQcaNGwdAUFAQV65cYdeuXdSoUQOAnTt3cuXKFWrVqmWUGTVqFAkJCXh7ewOwdu1a7OzsqFq1aq76naOgadpqV/djMplITk7OVQdERERERERERETkyXXt2jV+/fVX4/sTJ04QFxdHgQIFKF68OH369OGTTz6hdOnSlC5dmk8++QRHR0fatWsHgJubG127dqVfv354eHhQoEAB+vfvT/ny5WnYsCEAZcuWpUmTJnTr1o1Zs2YB8NZbb9GiRQv8/f0BaNy4MQEBAXTs2JHx48dz8eJF+vfvT7du3XB1dc3VNeUoaJqSkrc+JREREREREREREfm3e1JG5OzevZv69f9vhEXaXKidO3cmMjKSgQMHcvPmTXr06MGlS5eoWbMma9euxcXFxThn8uTJ5MuXj5CQEG7evMmLL75IZGQk1tbWRpkFCxbQu3dvGjduDNwd8T59+v+NWrC2tuaHH36gR48e1K5dGwcHB9q1a8eECRNyfU2m1NQ8lscuIiIi8i9y646le5BRXhvG+KT8MSCSW1duJFm6C2Z8675v6S5koOH58igkp+St/6/dvJ23RuA622sN8PvJy7fozW8OWqztL0IDLdZ2XvBAr8X169fZvHkzp06d4vbt22bHevfu/Ug6JiIiIiIiIiIi8jTTZ8uWk+ug6c8//0yzZs24ceMG169fp0CBApw/fx5HR0cKFSqkoKmIiIiIiIiIiIg80XK9hNr7779Py5YtuXjxIg4ODuzYsYPffvuNqlWrPtD8ACIiIiIiIiIiIiJ5Sa6DpnFxcfTr1w9ra2usra1JTEzEx8eHcePG8Z///Odx9FFEREREREREROSpYzKZLLY97XIdNLWxsTFuXOHChTl16hQAbm5uxtciIiIiIiIiIiIiT6pcz2lauXJldu/ejZ+fH/Xr12fIkCGcP3+eefPmUb58+cfRRxERERERERERkaeOEj4tJ9eZpp988gne3t4AjBgxAg8PD9555x3OnTvHf//730feQREREREREREREZF/Uq4zTatVq2Z87enpyapVqx5ph0RERERERERERASslGpqMbnONBUREckNX19fpkyZkqtztm3bRvny5bGxsaF169aPpV+PQ1hY2BPVXxEREREREclcrjNNS5Qoke0KWv/73/8eqkMiIv8WYWFhXL58meXLl1u6K49FvXr1qFSp0n0DorGxsTg5OeWq7r59+1KpUiV+/PFHnJ2dH6KXj8fJkycpUaIEP//8M5UqVTL2f/rpp6SmplquY/JY5fSdFxERERGRJ1+ug6Z9+vQx+z4pKYmff/6Z1atXM2DAgEfVLxEReQRu376Nra2t2b7k5GRMJhNWVv/MYANPT89cn3P8+HG6d+9OsWLFHrjdzK79cXNzc/tH2/s3SEpKwsbG5r7lLPE80+S0jyIiIiIij5pG51tOrv9ifu+998y2/v37s2DBAoYPH86RI0ceRx9FRP4V6tWrR+/evRk4cCAFChTAy8uL8PBwszKXL1/mrbfeonDhwtjb2xMYGMjKlSuN40uWLKFcuXLY2dnh6+vLxIkTzc739fVl5MiRhIWF4ebmRrdu3YiMjCR//vysXLmSgIAA7Ozs+O2337h9+zYDBw6kaNGiODk5UbNmTaKjo83q27ZtG3Xr1sXR0RF3d3eCg4O5dOkSYWFhbN68mU8//RSTyYTJZOLkyZOZXve9w/NNJhNffPEFL7/8Mo6OjpQuXZoVK1YAdzM4TSYTFy5coEuXLphMJiIjIwHYvHkzNWrUwM7ODm9vbz744APu3Lljdn979uxJ3759KViwII0aNSI6OhqTycSaNWuoXLkyDg4ONGjQgHPnzvHjjz9StmxZXF1dadu2LTdu3DDqWr16Nc8//zz58+fHw8ODFi1acPz4ceN4iRIlAKhcuTImk4l69eoBGYfnJyYm0rt3bwoVKoS9vT3PP/88sbGxxvG0/m3YsIFq1arh6OhIrVq17vv/0wMHDtCgQQMcHBzw8PDgrbfe4tq1a8bxtH5MmDABb29vPDw8ePfdd0lKSsq0vpMnT2JlZcXu3bvN9k+bNo1nnnnGyJ693zPIbCqGSpUqmb3nJpOJmTNn0qpVK5ycnBg5cmSmfcrsXQaIiYmhTp06ODg44OPjQ+/evbl+/brZeSNGjKBdu3Y4OztTpEgRpk2bZlb3qVOnaNWqFc7Ozri6uhISEsKff/5pHA8PD6dSpUrMnTuXkiVLYmdnR+fOnXP8zouIiIiIyJPvkaUZNW3alCVLljyq6kRE/pWioqJwcnJi586djBs3juHDh7Nu3ToAUlJSaNq0KTExMcyfP59Dhw4xZswYrK2tAdizZw8hISG8/vrrHDhwgPDwcD7++GMjqJhm/PjxBAYGsmfPHj7++GMAbty4wejRo/niiy/45ZdfKFSoEG+88Qbbtm1j4cKF7N+/n9dee40mTZpw7NgxAOLi4njxxRcpV64c27dvZ+vWrbRs2ZLk5GQ+/fRTgoKC6NatGwkJCSQkJODj45Pj+zBs2DBCQkLYv38/zZo1o3379ly8eBEfHx8SEhJwdXVlypQpJCQkEBoayu+//06zZs2oXr06+/btY8aMGcyZMydDwC0qKop8+fKxbds2Zs2aZewPDw9n+vTpxMTEcPr0aUJCQpgyZQpfffUVP/zwA+vWrTMLrF2/fp2+ffsSGxvLhg0bsLKy4uWXXyYlJQWAXbt2AbB+/XoSEhJYunRpptc5cOBAlixZQlRUFHv37qVUqVIEBwdz8eJFs3IffvghEydOZPfu3eTLl48uXbpkee9u3LhBkyZNcHd3JzY2lsWLF7N+/Xp69uxpVm7Tpk0cP36cTZs2ERUVRWRkZIZ3JY2vry8NGzYkIiLCbH9ERARhYWGYTKYcP4OcGDp0KK1ateLAgQPZXuu97/KBAwcIDg6mTZs27N+/n2+++YatW7dmuPbx48dToUIF9u7dy+DBg3n//feNn7PU1FRat27NxYsX2bx5M+vWreP48eOEhoaa1fHrr7+yaNEilixZQlxcHFOnTn2od15ERERE5EGkfWBvie1pl+vh+Vn59ttvKVCgwKOqTkTkX6lChQoMHToUgNKlSzN9+nQ2bNhAo0aNWL9+Pbt27SI+Ph4/Pz8ASpYsaZw7adIkXnzxRSMQ6ufnx6FDhxg/fjxhYWFGuQYNGtC/f3/j+61bt5KUlMTnn39OxYoVgbvD37/++mvOnDlDkSJFAOjfvz+rV68mIiKCTz75hHHjxlGtWjU+//xzo65y5coZX9va2uLo6IiXl1eu70NYWBht27YF4JNPPmHatGns2rWLJk2a4OXlhclkws3Nzaj7888/x8fHh+nTp2MymShTpgx//PEHgwYNYsiQIcZUA6VKlWLcuHFGO2fPngVg5MiR1K5dG4CuXbsyePBgjh8/btzfV199lU2bNjFo0CAAXnnlFbP+zpkzh0KFCnHo0CECAwONKQc8PDyyvP7r168zY8YMIiMjadq0KQCzZ89m3bp1zJkzx2xKm1GjRlG3bl0APvjgA5o3b86tW7ewt7fPUO+CBQu4efMmX375pTFX7PTp02nZsiVjx46lcOHCALi7uzN9+nSsra0pU6YMzZs3Z8OGDUbG5r3efPNNunfvzqRJk7Czs2Pfvn3ExcUZAeGcPoOcaNeuXbbB0jT3vsudOnWiXbt2xlRBpUuXZurUqdStW5cZM2YY96t27dp88MEHwN2fk23btjF58mTj52z//v2cOHHCCHrOmzePcuXKERsbS/Xq1YG70wHMmzfPbHqJh3nnRURERETkyZLrTNPKlStTpUoVY6tcuTLe3t785z//4T//+c/j6KOIyL9GhQoVzL739vbm3LlzwN3MzmLFihkB03vFx8cbgb80tWvX5tixYyQnJxv7qlWrluFcW1tbs7b37t1Lamoqfn5+ODs7G9vmzZuNYehpmaaPQ/q+ODk54eLiYtyHzMTHxxMUFGT2aWft2rW5du0aZ86cMfZldu33tle4cGEcHR3NAtKFCxc2a//48eO0a9eOkiVL4urqagzHP3XqVI6v8fjx4yQlJZk9MxsbG2rUqEF8fHyW/fP29gbI8n7Ex8dTsWJFs8W1ateuTUpKitmw/nLlyhlZymn1ZnePW7duTb58+Vi2bBkAc+fOpX79+vj6+hrt5uQZ5ERWz+l+5fbs2UNkZKTZOxscHExKSgonTpwwygUFBZmdFxQUZNzz+Ph4fHx8zLJEAwICyJ8/v9lzeeaZZx5oPt7ExESuXr1qtiUmJua6HhERERERsaxcZ5q2atXK7A8mKysrPD09qVevHmXKlHmknRMR+be5dzEZk8lkDPl2cHDI9tzU1NQMQyQyW6k9s5XqHRwczM5NSUnB2tqaPXv2mAXWAGO1+vv152Fkdx8yk921p9+f2bXf257JZLpv+y1btsTHx4fZs2dTpEgRUlJSCAwM5Pbt2/e5suz7l9W13Ns/IMv7kdn59557b51px7K7x7a2tnTs2JGIiAjatGnDV199ZTY/aU6egZWVVYZ3MrN5VLN6Tvcrl5KSwttvv03v3r0zlC1evHi2daX1Mav7d+/+nPbxXqNHj2bYsGFm+z78eCgfDQl/oPpERERE5On2zyzfK5nJddD03kVLRETk0ahQoQJnzpzh6NGjmWabBgQEsHXrVrN9MTEx+Pn5ZQh83k/lypVJTk7m3LlzvPDCC1n2Z8OGDRkCQGlsbW3NMlwfp4CAAJYsWWIW2IqJicHFxYWiRYs+0rYuXLhAfHw8s2bNMu7Nvfc9bRX37K6/VKlS2NrasnXrVtq1awfcDSDu3r3bGF7+IAICAoiKiuL69etGYG/btm1YWVllmaWcU2+++SaBgYF8/vnnJCUl0aZNG7N27/cMPD09SUhIMM65evWqWQbow6pSpQq//PILpUqVyrbcjh07Mnyf9sFuQEAAp06d4vTp00a26aFDh7hy5Qply5bNtt6cvPODBw+mb9++ZvtSre2yPUdERERERPKeXAesra2tMx3ed+HChVz/0S4iIv+nbt261KlTh1deeYV169Zx4sQJfvzxR1avXg1Av3792LBhAyNGjODo0aNERUUxffp0szkfc8rPz4/27dvTqVMnli5dyokTJ4iNjWXs2LGsWrUKuBv8iY2NpUePHuzfv5/Dhw8zY8YMzp8/D9xdPGjnzp2cPHmS8+fPZ5vF+LB69OjB6dOn6dWrF4cPH+a7775j6NCh9O3bN1dzaeaEu7s7Hh4e/Pe//+XXX39l48aNGYJghQoVwsHBgdWrV/Pnn39y5cqVDPU4OTnxzjvvMGDAAFavXs2hQ4fo1q0bN27coGvXrg/cv/bt22Nvb0/nzp05ePAgmzZtolevXnTs2NGYz/RBlS1blueee45BgwbRtm1bs2zjnDyDBg0aMG/ePLZs2cLBgwfp3LnzI/23waBBg9i+fTvvvvsucXFxHDt2jBUrVtCrVy+zctu2bWPcuHEcPXqUzz77jMWLF/Pee+8B0LBhQypUqED79u3Zu3cvu3btolOnTtStW/e+0wbk5J23s7PD1dXVbLOzU9BURERERB6MFoKynFz/pZnZUFC4O4dXWuaNiIg8mCVLllC9enXatm1LQEAAAwcONDLbqlSpwqJFi1i4cCGBgYEMGTKE4cOHmy0ClRsRERF06tSJfv364e/vz0svvcTOnTuN7Ds/Pz/Wrl3Lvn37qFGjBkFBQXz33Xfky3d3kEL//v2xtrYmICAAT0/PXM33mVtFixZl1apV7Nq1i4oVK9K9e3e6du3KRx999MjbsrKyYuHChezZs4fAwEDef/99xo8fb1YmX758TJ06lVmzZlGkSBFatWqVaV1jxozhlVdeoWPHjlSpUoVff/2VNWvW4O7u/sD9c3R0ZM2aNVy8eJHq1avz6quv8uKLLzJ9+vQHrjO9rl27cvv27QwLNeXkGQwePJg6derQokULmjVrRuvWrXn22WcfSb/gbvbz5s2bOXbsGC+88AKVK1fm448/NuaBTdOvXz/27NlD5cqVGTFiBBMnTiQ4OBi4+4/O5cuX4+7uTp06dWjYsCElS5bkm2++uW/7/+Q7LyIiIiIilmVKzSoKeo+pU6cC8P777zNixAhjzju4Ozzxp59+4uTJk/z888+Pp6ciIiLy2I0aNYqFCxdy4MABS3flgfj6+tKnT5+HmgLhUbt1x9I9yCglZ//8+8dYKZNB/qWu3Mg4r7Ml+dZ939JdyOBS7KP50E+ebskpeev/azdv/zNTWOWUs32uZ2Z86uTlW9Tnu8MWa3tKq6d77aIcvxaTJ08G7maazpw502y4na2tLb6+vsycOfPR91BEREQeu2vXrhEfH8+0adMYMWKEpbsjIiIiIiJiUTkOmqYt5FC/fn2WLl36UEMLRUREJG/p2bMnX3/9Na1bt84wNF9ERERERORpk+sE5E2bNj2OfoiIiIgFRUZGEhkZaeluPLSTJ09augsiIiIiIo+MlWYxsphcLwT16quvMmbMmAz7x48fz2uvvfZIOiUiIiIiIiIiIiJiKbkOmm7evJnmzZtn2N+kSRN++umnR9IpERERERERERGRp53JZLLY9rTLddD02rVr2NraZthvY2PD1atXH0mnRERERERERERERCwl10HTwMBAvvnmmwz7Fy5cSEBAwCPplIiIiIiIiIiIiIil5HohqI8//phXXnmF48eP06BBAwA2bNjAV199xbfffvvIOygiIiIiIiIiIvI00kJQlpProOlLL73E8uXL+eSTT/j2229xcHCgYsWKbNy4EVdX18fRRxEREREREREREZF/TK6DpgDNmzc3FoO6fPkyCxYsoE+fPuzbt4/k5ORH2kEREREREREREZGnkdZjspxcz2maZuPGjXTo0IEiRYowffp0mjVrxu7dux9l30RERERERERERET+cbnKND1z5gyRkZHMnTuX69evExISQlJSEkuWLNEiUCIiIiIiIiIiIo+QlVJNLSbHmabNmjUjICCAQ4cOMW3aNP744w+mTZv2OPsmIiIiIiIiIiIi8o/Lcabp2rVr6d27N++88w6lS5d+nH0SERERkcfIhDIWRP4Jbo42lu6CmUux0y3dBZHHwjqPLS/ubP9Ay8eISB6T40zTLVu28Pfff1OtWjVq1qzJ9OnT+euvvx5n30RERERERERERJ5aVhbcnnY5vgdBQUHMnj2bhIQE3n77bRYuXEjRokVJSUlh3bp1/P3334+znyIiIiIiIiIiIiL/CFNqamrqg5585MgR5syZw7x587h8+TKNGjVixYoVj7J/IiIiIk+0W3cs3YOMHvxff4+H1jcQERERS8rLMyp8+ONRi7U9qqmfxdrOCx4q29bf359x48Zx5swZvv7660fVJxERERERERERERGLeSRTFFhbW9O6dWtlmYqIiIiIiIiIiMgTLw8nIIuIiIiIiIiIiDy9rDSPkcVoMSwRERERERERERGRdJRpKiIiIiIiIiIikgcp0dRylGkqIiIiIiIiIiIiko4yTUVERERERERERPIgK2WaWowyTUVERO4RHR2NyWTi8uXL/3jbYWFhtG7d+qHqqFevHn369Hkk/XnSnDx5EpPJRFxcnKW7IiIiIiIiTzAFTUVEJE95FEHDf6uwsDBMJlO2m6VFRkaSP3/+HJe/efMm7u7uFChQgJs3b+aqrczeFR8fHxISEggMDMxVXSIiIiIiIukpaCoiIvKE+PTTT0lISDA2gIiIiAz7HkRSUtKj6mauLFmyhMDAQAICAli6dOlD12dtbY2Xlxf58mkGIhERERF58lmZTBbbnnYKmoqIyBPl0KFDNGvWDGdnZwoXLkzHjh05f/68cfzvv/+mffv2ODk54e3tzeTJkzMMV58/fz7VqlXDxcUFLy8v2rVrx7lz53LVj0mTJlG+fHmcnJzw8fGhR48eXLt2zTielnG5Zs0aypYti7OzM02aNDELbCYnJ9O3b1/y58+Ph4cHAwcOJDU1Ncs23dzc8PLyMjaA/PnzZ9gHkJKSwsCBAylQoABeXl6Eh4eb1WUymZg5cyatWrXCycmJkSNHAvD9999TtWpV7O3tKVmyJMOGDePOnTs5uu7o6GjeeOMNrly5YmS+3tvuvebMmUOHDh3o0KEDc+bMyXD8l19+oXnz5ri6uuLi4sILL7zA8ePHCQ8PJyoqiu+++85oKzo6OtPh+Zs3b6ZGjRrY2dnh7e3NBx98YHZN9erVo3fv3tner/DwcIoXL46dnR1FihShd+/e2V6XiIiIiIg82RQ0FRGRJ0ZCQgJ169alUqVK7N69m9WrV/Pnn38SEhJilOnbty/btm1jxYoVrFu3ji1btrB3716zem7fvs2IESPYt28fy5cv58SJE4SFheWqL1ZWVkydOpWDBw8SFRXFxo0bGThwoFmZGzduMGHCBObNm8dPP/3EqVOn6N+/v3F84sSJzJ07lzlz5rB161YuXrzIsmXLcn9jMhEVFYWTkxM7d+5k3LhxDB8+nHXr1pmVGTp0KK1ateLAgQN06dKFNWvW0KFDB3r37s2hQ4eYNWsWkZGRjBo1KkfXXatWLaZMmYKrq6uR+Zr+eu91/Phxtm/fTkhICCEhIcTExPC///3POP77779Tp04d7O3t2bhxI3v27KFLly7cuXOH/v37ExISYgSiExISqFWrVoY2fv/9d5o1a0b16tXZt28fM2bMYM6cOUaQOCf369tvv2Xy5MnMmjWLY8eOsXz5csqXL5/7hyIiIiIikksmk+W2p53GromIyBNjxowZVKlShU8++cTYN3fuXHx8fDh69Cje3t5ERUXx1Vdf8eKLLwJ3h68XKVLErJ4uXboYX5csWZKpU6dSo0YNrl27hrOzc476kj5ztUSJEowYMYJ33nmHzz//3NiflJTEzJkzefbZZwHo2bMnw4cPN45PmTKFwYMH88orrwAwc+ZM1qxZk8O7kb0KFSowdOhQAEqXLs306dPZsGEDjRo1Msq0a9fO7F507NiRDz74gM6dOwN3782IESMYOHCgUVd2121ra4ubmxsmk8ks6zUrc+fOpWnTpri7uwPQpEkT5s6dawQ0P/vsM9zc3Fi4cCE2NjYA+Pn5Gec7ODiQmJiYbVuff/45Pj4+TJ8+HZPJRJkyZfjjjz8YNGgQQ4YMwcrK6r7369SpU3h5edGwYUNsbGwoXrw4NWrUuO/1iYiIiIjIk0uZpiIi8sTYs2cPmzZtwtnZ2djKlCkD3M1a/N///kdSUpJZQMvNzQ1/f3+zen7++WdatWrFM888g4uLC/Xq1QPg1KlTOe7Lpk2baNSoEUWLFsXFxYVOnTpx4cIFrl+/bpRxdHQ0AqYA3t7exjQAV65cISEhgaCgION4vnz5qFatWs5vSDYqVKhg9n36ttPc29aePXsYPny42f3t1q0bCQkJ3LhxI8fXnRPJyclERUXRoUMHY1+HDh2IiooiOTkZgLi4OF544QUjYPog4uPjCQoKMlskq3bt2ly7do0zZ84Y+7K7X6+99ho3b96kZMmSdOvWjWXLlpkN708vMTGRq1evmm2JiYkP3H8REREREbEMBU1FROSJkZKSQsuWLYmLizPbjh07Rp06dYz5QO9dRT79PKHXr1+ncePGODs7M3/+fGJjY40h8bdv385RP3777TeaNWtGYGAgS5YsYc+ePXz22WeA+YJK9wb7TCZTtnOWPkqZtZ2SkmK2z8nJyez7lJQUhg0bZnZvDxw4wLFjx7C3t8/xdefEmjVr+P333wkNDSVfvnzky5eP119/nTNnzrB27Vrgbibpw0pNTc3yfUi/P7v75ePjw5EjR/jss89wcHCgR48e1KlTJ9NrHj16NG5ubmbb+LGjH/o6REREROTpZGWy3Pa00/B8ERF5YlSpUoUlS5bg6+ub6erozz77LDY2NuzatQsfHx8Arl69yrFjx6hbty4Ahw8f5vz584wZM8Yos3v37lz1Y/fu3dy5c4eJEycaw7sXLVqUqzrc3Nzw9vZmx44d1KlTB4A7d+6wZ88eqlSpkqu6HpUqVapw5MgRSpUqlenxnFy3ra2tkSmanTlz5vD666/z4Ycfmu0fM2YMc+bMoWnTplSoUIGoqCiSkpIyzTbNSVsBAQEsWbLELHgaExODi4sLRYsWvW8/0zg4OPDSSy/x0ksv8e6771KmTBkOHDiQ4VkNHjyYvn37mu1LtbbLcTsiIiIiIpI3KGgqIiJ5zpUrV8xWPwcoUKAA7777LrNnz6Zt27YMGDCAggUL8uuvv7Jw4UJmz56Ni4sLnTt3ZsCAARQoUIBChQoxdOhQrKysjIBZ8eLFsbW1Zdq0aXTv3p2DBw8yYsSIXPXv2Wef5c6dO0ybNo2WLVuybds2Zs6cmevrfO+99xgzZgylS5embNmyTJo0icuXL+e6nkdlyJAhtGjRAh8fH1577TWsrKzYv38/Bw4cYOTIkTm6bl9fX65du8aGDRuoWLEijo6OODo6mpX566+/+P7771mxYgWBgYFmxzp37kzz5s3566+/6NmzJ9OmTeP1119n8ODBuLm5sWPHDmrUqIG/vz++vr6sWbOGI0eO4OHhgZubW4Zr6tGjB1OmTKFXr1707NmTI0eOMHToUPr27WsEfu8nMjKS5ORkatasiaOjI/PmzcPBwYFnnnkmQ1k7Ozvs7MyDpLcyH8kvIiIiInJfJpTyaSkani8iInlOdHQ0lStXNtuGDBlCkSJF2LZtG8nJyQQHBxMYGMh7772Hm5ubEQCbNGkSQUFBtGjRgoYNG1K7dm3Kli2Lvb09AJ6enkRGRrJ48WICAgIYM2YMEyZMyFX/KlWqxKRJkxg7diyBgYEsWLCA0aNzPwS7X79+dOrUibCwMIKCgnBxceHll1/OdT2PSnBwMCtXrmTdunVUr16d5557jkmTJhnBwZxcd61atejevTuhoaF4enoybty4DO18+eWXODk5GYt1pVe/fn1cXFyYN28eHh4ebNy4kWvXrlG3bl2qVq3K7NmzjazTbt264e/vT7Vq1fD09GTbtm0Z6itatCirVq1i165dVKxYke7du9O1a1c++uijHN+X/PnzM3v2bGrXrk2FChXYsGED33//PR4eHjmuQ0REREREniym1H9qcjURERELuH79OkWLFmXixIl07drV0t2Rp1BezDTNa//6MymBQkRERCzIPg+Pw/5kw3GLtf2fF5+9f6F/sTz8WoiIiOTezz//zOHDh6lRowZXrlxh+PDhALRq1crCPRMREREREckdLchkOQqaiojIv86ECRM4cuQItra2VK1alS1btlCwYEFLd0tERERERESeEAqaiojIv0rlypXZs2ePpbshIiIiIiLy0JRpajlaCEpEREREREREREQkHWWaioiIiIiIiIiI5EEmrZhpMco0FREREREREREREUlHQVMRERERERERERGRdDQ8X0REREREREREJA/SQlCWo0xTERERERERERERkXSUaSoiIiIiIiIiIpIHaR0oy1GmqYiIiIiIiIiIiEg6CpqKiIiIiIiIiIiIpKPh+SIiIiIiIiIiInmQlcbnW4yCpiIiIiJPmbz2b+/UVEv3IKO8do9E5J9x5UaSpbuQgauDjaW7YEa/H0XkaaGgqYiIiIiIiIiISB5kpQ8qLEZzmoqIiIiIiIiIiMgD8/X1xWQyZdjeffddAMLCwjIce+6558zqSExMpFevXhQsWBAnJydeeuklzpw5Y1bm0qVLdOzYETc3N9zc3OjYsSOXL19+LNekoKmIiIiIiIiIiEgeZDJZbsuN2NhYEhISjG3dunUAvPbaa0aZJk2amJVZtWqVWR19+vRh2bJlLFy4kK1bt3Lt2jVatGhBcnKyUaZdu3bExcWxevVqVq9eTVxcHB07dnzwG5wNDc8XERERERERERGRB+bp6Wn2/ZgxY3j22WepW7eusc/Ozg4vL69Mz79y5Qpz5sxh3rx5NGzYEID58+fj4+PD+vXrCQ4OJj4+ntWrV7Njxw5q1qwJwOzZswkKCuLIkSP4+/s/0mtSpqmIiIiIiIiIiIiYSUxM5OrVq2ZbYmLifc+7ffs28+fPp0uXLpjSpaxGR0dTqFAh/Pz86NatG+fOnTOO7dmzh6SkJBo3bmzsK1KkCIGBgcTExACwfft23NzcjIApwHPPPYebm5tR5lFS0FRERERERERERCQPssJksW306NHG3KFp2+jRo+/b5+XLl3P58mXCwsKMfU2bNmXBggVs3LiRiRMnEhsbS4MGDYwg7NmzZ7G1tcXd3d2srsKFC3P27FmjTKFChTK0V6hQIaPMo6Th+SIiIiIiIiIiImJm8ODB9O3b12yfnZ3dfc+bM2cOTZs2pUiRIsa+0NBQ4+vAwECqVavGM888ww8//ECbNm2yrCs1NdUsW9WUyWSr95Z5VBQ0FRERERERERERyYMeQywwx+zs7HIUJE3vt99+Y/369SxdujTbct7e3jzzzDMcO3YMAC8vL27fvs2lS5fMsk3PnTtHrVq1jDJ//vlnhrr++usvChcunKt+5oSG54uIiIiIiIiIiMhDi4iIoFChQjRv3jzbchcuXOD06dN4e3sDULVqVWxsbFi3bp1RJiEhgYMHDxpB06CgIK5cucKuXbuMMjt37uTKlStGmUdJmaYiIiIiIiIiIiLyUFJSUoiIiKBz587ky/d/Icdr164RHh7OK6+8gre3NydPnuQ///kPBQsW5OWXXwbAzc2Nrl270q9fPzw8PChQoAD9+/enfPnyNGzYEICyZcvSpEkTunXrxqxZswB46623aNGiBf7+/o/8epRpKiIi8gTw9fVlypQplu6GiIiIiIj8g6xMlttya/369Zw6dYouXbqY7be2tubAgQO0atUKPz8/OnfujJ+fH9u3b8fFxcUoN3nyZFq3bk1ISAi1a9fG0dGR77//Hmtra6PMggULKF++PI0bN6Zx48ZUqFCBefPmPfD9zY4pNTU19bHULCIikseEhYURFRXF22+/zcyZM82O9ejRgxkzZtC5c2ciIyMt08Fs/PXXXzg5OeHo6Gjprjy1IiMj6dOnD5cvX87VebfuPJ7+/JvkxX+NWnL+MBGxnCs3kizdhQxcHWws3QUz+v0o/0b2eXgc9sztJy3WdvcgX4u1nRco01RERJ4qPj4+LFy4kJs3bxr7bt26xddff03x4sUt2LPseXp6KmAqIiIiIvKUsTKZLLY97RQ0FRGRp0qVKlUoXry42WqOS5cuxcfHh8qVK5uVXb16Nc8//zz58+fHw8ODFi1acPz4ceP4yZMnMZlMLF26lPr16+Po6EjFihXZvn27UebChQu0bduWYsWK4ejoSPny5fn666/N2vn7779p3749Tk5OeHt7M3nyZOrVq0efPn2MMvcOzzeZTHzxxRe8/PLLODo6Urp0aVasWGFW74oVKyhdujQODg7Ur1+fqKgoTCZTtpmSkyZNonz58jg5OeHj40OPHj24du2acTwyMpL8+fOzZs0aypYti7OzM02aNCEhISHLOpOTk+natSslSpTAwcEBf39/Pv300wzl5s6dS7ly5bCzs8Pb25uePXsaxy5fvsxbb71F4cKFsbe3JzAwkJUrVxrHlyxZYpzr6+vLxIkTzeo2mUwsX77cbF/+/PmNrOL7Pcvo6GjeeOMNrly5gslkwmQyER4enuU1i4iIiIjIk01BUxEReeq88cYbREREGN/PnTs3w7w7ANevX6dv377ExsayYcMGrKysePnll0lJSTEr9+GHH9K/f3/i4uLw8/Ojbdu23Llzd0z2rVu3qFq1KitXruTgwYO89dZbdOzYkZ07dxrn9+3bl23btrFixQrWrVvHli1b2Lt3732vY9iwYYSEhLB//36aNWtG+/btuXjxInA3CPjqq6/SunVr4uLiePvtt/nwww/vW6eVlRVTp07l4MGDREVFsXHjRgYOHGhW5saNG0yYMIF58+bx008/cerUKfr3759lnSkpKRQrVoxFixZx6NAhhgwZwn/+8x8WLVpklJkxYwbvvvsub731FgcOHGDFihWUKlXKOL9p06bExMQwf/58Dh06xJgxY4y5jfbs2UNISAivv/46Bw4cIDw8nI8//viBplnI6lnWqlWLKVOm4OrqSkJCAgkJCdles4iIiIjIo2AyWW572uXhWRtEREQej44dOzJ48GAju3Dbtm0sXLiQ6Ohos3KvvPKK2fdz5syhUKFCHDp0iMDAQGN///79ad68OXA3kFmuXDl+/fVXypQpQ9GiRc2Ca7169WL16tUsXryYmjVr8vfffxMVFcVXX33Fiy++CEBERARFihS573WEhYXRtm1bAD755BOmTZvGrl27aNKkCTNnzsTf35/x48cD4O/vz8GDBxk1alS2dabPbi1RogQjRozgnXfe4fPPPzf2JyUlMXPmTJ599lkAevbsyfDhw7Os08bGhmHDhpnVGxMTw6JFiwgJCQFg5MiR9OvXj/fee88oV716deDuhPK7du0iPj4ePz8/AEqWLGmUmzRpEi+++CIff/wxAH5+fhw6dIjx48cTFhaW7fXeK7tn6ebmhslkwsvLK1d1ioiIiIjIk0eZpiIi8tQpWLAgzZs3JyoqioiICJo3b07BggUzlDt+/Djt2rWjZMmSuLq6UqJECQBOnTplVq5ChQrG197e3gCcO3cOuDs0fdSoUVSoUAEPDw+cnZ1Zu3atUcf//vc/kpKSqFGjhlGHm5sb/v7+972O9O06OTnh4uJitHvkyBEj6JgmfRtZ2bRpE40aNaJo0aK4uLjQqVMnLly4wPXr140yjo6ORsA07ZrT2s3KzJkzqVatGp6enjg7OzN79mzjHpw7d44//vjDCBrfKy4ujmLFihkB03vFx8dTu3Zts321a9fm2LFjJCcn3/ea08vuWeZEYmIiV69eNdsSExNz1QcREREREbE8BU1FROSp1KVLFyIjI4mKisp0aD5Ay5YtuXDhArNnz2bnzp3GkPrbt2+blbOx+b9VbU3/fxxL2hD+iRMnMnnyZAYOHMjGjRuJi4sjODjYqCP1/y8bbrpn/EtqDpYTT99uWh1p7aampua6zt9++41mzZoRGBjIkiVL2LNnD5999hlwN7s0u3azq3vRokW8//77dOnShbVr1xIXF8cbb7xh3AMHB4ds+3W/4zm51sz6mP6a0mT3LHNi9OjRuLm5mW3jx47O8fkiIiIiIulpISjL0fB8ERF5KjVp0sQI2gUHB2c4fuHCBeLj45k1axYvvPACAFu3bs11O1u2bKFVq1Z06NABuBuAO3bsGGXLlgXg2WefxcbGhl27duHj4wPA1atXOXbsGHXr1n2gawMoU6YMq1atMtu3e/fubM/ZvXs3d+7cYeLEiVhZ3f1cNf28ow9qy5Yt1KpVix49ehj70i+o5eLigq+vLxs2bKB+/foZzq9QoQJnzpzh6NGjmWabBgQEZHg2MTEx+Pn5GfOeenp6mi1WdezYMW7cuJGr67C1tb1v5urgwYPp27ev2b5Ua7tctSMiIiIiIpanoKmIiDyVrK2tiY+PN76+l7u7Ox4eHvz3v//F29ubU6dO8cEHH+S6nVKlSrFkyRJiYmJwd3dn0qRJnD171giauri40LlzZwYMGECBAgUoVKgQQ4cOxcrKKkP2ZG68/fbbTJo0iUGDBtG1a1fi4uKMhZGyqvfZZ5/lzp07TJs2jZYtW7Jt2zZmzpz5wH1IU6pUKb788kvWrFlDiRIlmDdvHrGxscZ0BwDh4eF0796dQoUK0bRpU/7++2+2bdtGr169qFu3LnXq1OGVV15h0qRJlCpVisOHD2MymWjSpAn9+vWjevXqjBgxgtDQULZv38706dPN5mFt0KAB06dP57nnniMlJYVBgwZlyJi9H19fX65du8aGDRuoWLEijo6OODo6mpWxs7PDzs48SHrrzgPcNBERERERtCCTJWl4voiIPLVcXV1xdXXN9JiVlRULFy5kz549BAYG8v777xuLKuXGxx9/TJUqVQgODqZevXp4eXnRunVrszKTJk0iKCiIFi1a0LBhQ2rXrk3ZsmWxt7d/kMsC7i629O2337J06VIqVKjAjBkz+PDDDwEyBPXSVKpUiUmTJjF27FgCAwNZsGABo0c//NDy7t2706ZNG0JDQ6lZsyYXLlwwyzoF6Ny5M1OmTOHzzz+nXLlytGjRgmPHjhnHlyxZQvXq1Wnbti0BAQEMHDjQyPqsUqUKixYtYuHChQQGBjJkyBCGDx9utgjUxIkT8fHxoU6dOrRr147+/ftnCHjeT61atejevTuhoaF4enoybty4B78pIiIiIiKSp5lSczJpmoiIiPxjrl+/TtGiRZk4cSJdu3Z9ZPWOGjWKmTNncvr06UdWp9yfMk3vLy/+a1RZHSJPpys3Ms53bWmuDrkbGfG46fej/BvZ5+Fx2HNjT92/0GPSpXpxi7WdF+Th10JEROTp8PPPP3P48GFq1KjBlStXGD58OACtWrV6qHo///xzqlevjoeHB9u2bWP8+PH07NnzUXRZRERERET+ARoibjkKmoqIiOQBEyZM4MiRI9ja2lK1alW2bNlCwYIFH6rOY8eOMXLkSC5evEjx4sXp168fgwcPfkQ9FhERERER+ffS8HwRERGRx0jD8+8vL/5rVMNPRZ5OGp5/f/r9KP9GeXl4ftRuy02t1bmaj8XazguU5SsiIiIiIiIiIiKSTh6OpYuIiIiIiIiIiDy9lNxtOco0FREREREREREREUlHQVMRERERERERERGRdDQ8X0REREREREREJA+y0uprFqNMUxEREREREREREZF0lGkqIiIiIiIiIiKSBynP1HKUaSoiIiIiIiIiIiKSjoKmIiIiIiIiIiIiIuloeL6IiIiIiIiIiEgepHWgLEdBUxERERGxKP0xICJ5hauDjaW7kEFKaqqlu2DGWr+0ReQpoaCpiIiIiIiIiIhIHmTSBxUWozlNRURERERERERERNJRpqmIiIiIiIiIiEgepGxHy9G9FxEREREREREREUlHQVMRERERERERERGRdDQ8X0REREREREREJA/SQlCWo0xTERERERERERERkXSUaSoiIiIiIiIiIpIHKc/UcpRpKiIiIiIiIiIiIpKOgqYiIiIiIiIiIiIi6Wh4voiIiIiIiIiISB6khaAsR5mmIiLyVDOZTCxfvtzS3cggr/ZLRERERETkaaCgqYhIHhcWFobJZMJkMmFjY0PJkiXp378/169ft3TXHlp0dDQmk4nLly9buitZSn//02+//vqrpbuWpfDwcEwmE02aNMlwbNy4cZhMJurVq/fPd+wJFx4eTqVKlSzdDRERERF5ilhZcHvaaXi+iMgToEmTJkRERJCUlMSWLVt48803uX79OjNmzLB0154Kafc/PU9PTwv1Jme8vb3ZtGkTZ86coVixYsb+iIgIihcvbsGeiYiIiIiI5H0KHIuIPAHs7Ozw8vLCx8eHdu3a0b59e2Po9vz586lWrRouLi54eXnRrl07zp07B0BqaiqlSpViwoQJZvUdPHgQKysrjh8/DtwdCj5r1ixatGiBo6MjZcuWZfv27fz666/Uq1cPJycngoKCjPJpvv/+e6pWrYq9vT0lS5Zk2LBh3LlzxzhuMpn44osvePnll3F0dKR06dKsWLECgJMnT1K/fn0A3N3dMZlMhIWFZXr9Fy5coG3bthQrVgxHR0fKly/P119/bVamXr169O7dm4EDB1KgQAG8vLwIDw83K3Ps2DHq1KmDvb09AQEBrFu3Llf3P/1mbW1NWFgYrVu3Nivbp08fsyzOR9Gv27dv07NnT7y9vbG3t8fX15fRo0dn2+dChQrRuHFjoqKijH0xMTGcP3+e5s2bm5WNjY2lUaNGFCxYEDc3N+rWrcvevXvNymT3LAGSk5Pp2rUrJUqUwMHBAX9/fz799FOzOu7cuUPv3r3Jnz8/Hh4eDBo0iM6dO5vdw9TUVMaNG0fJkiVxcHCgYsWKfPvtt8bxtOzkNWvWULlyZRwcHGjQoAHnzp3jxx9/pGzZsri6utK2bVtu3LiR63o3bNhAtWrVcHR0pFatWhw5cgSAyMhIhg0bxr59+4xs48jIyGyfgYiIiIjIw8ps1Ns/tT3tFDQVEXkCOTg4kJSUBNwNqI0YMYJ9+/axfPlyTpw4YQQfTSYTXbp0yZAlOXfuXF544QWeffZZY9+IESPo1KkTcXFxlClThnbt2vH2228zePBgdu/eDUDPnj2N8mvWrKFDhw707t2bQ4cOMWvWLCIjIxk1apRZW8OGDSMkJIT9+/fTrFkz2rdvz8WLF/Hx8WHJkiUAHDlyhISEhAxBtjS3bt2iatWqrFy5koMHD/LWW2/RsWNHdu7caVYuKioKJycndu7cybhx4xg+fLgRgExJSaFNmzZYW1uzY8cOZs6cyaBBg3J76x/Iw/Zr6tSprFixgkWLFnHkyBHmz5+Pr6/vfdvt0qWLWWBv7ty5tG/fHltbW7Nyf//9N507d2bLli3s2LGD0qVL06xZM/7++2+zclk9y7TrKFasGIsWLeLQoUMMGTKE//znPyxatMg4f+zYsSxYsICIiAi2bdvG1atXM8zb+tFHHxEREcGMGTP45ZdfeP/99+nQoQObN282KxceHs706dOJiYnh9OnThISEMGXKFL766it++OEH1q1bx7Rp03Jd74cffsjEiRPZvXs3+fLlo0uXLgCEhobSr18/ypUrR0JCAgkJCYSGht73GYiIiIiIyJPJlJqammrpToiISNbCwsK4fPmyEVzatWsXzZo148UXX+Sbb77JUD42NpYaNWrw999/4+zsTEJCAj4+PsTExFCjRg2SkpIoWrQo48ePp3PnzsDd4OpHH33EiBEjANixYwdBQUHMmTPHCBotXLiQN954g5s3bwJQp04dmjZtyuDBg42258+fz8CBA/njjz8yrff69eu4uLiwatUqmjRpQnR0NPXr1+fSpUvkz58/V/elefPmlC1b1siirVevHsnJyWzZssUoU6NGDRo0aMCYMWNYu3YtzZo14+TJk8Zw9dWrV9O0aVOWLVuWIWM0/f2fP38+9vb2xr6mTZuyePHiDM8G7maaxsXFER0d/cj61bt3b3755RfWr1+fo098w8PDWb58ObGxsRQrVozFixdTtWpVvL292bp1K3PnzjXr472Sk5Nxd3fnq6++okWLFsD9n2Vm3n33Xf78808jo9PLy4v+/fvTv39/o52SJUtSuXJlli9fzvXr1ylYsCAbN24kKCjIqOfNN9/kxo0bfPXVV8Y7s379el588UUAxowZw+DBgzl+/DglS5YEoHv37pw8eZLVq1c/cL2rVq2iefPm3Lx5E3t7e+O+xsXFZXnvExMTSUxMNNuXam2HnZ1dlueIiEjekRf/Ok7JY52ytlL2mfz72OfhySuX7T9rsbZfruBlsbbzgjz8WoiISJqVK1fi7OzMnTt3SEpKolWrVkYW3c8//0x4eDhxcXFcvHiRlJQUAE6dOkVAQADe3t40b96cuXPnUqNGDVauXMmtW7d47bXXzNqoUKGC8XXhwoUBKF++vNm+W7ducfXqVVxdXdmzZw+xsbFmmaXJycncunWLGzdu4OjomKFeJycnXFxcjOkDcio5OZkxY8bwzTff8PvvvxuBKScnpyyvAe7O65nWVnx8PMWLFzeb3zN9AC079evXN5s/9t527+dh+xUWFkajRo3w9/enSZMmtGjRgsaNG9+3XRsbGzp06EBERAT/+9//8PPzy9AXgHPnzjFkyBA2btzIn3/+SXJyMjdu3ODUqVNZXkdmz3LmzJl88cUX/Pbbb9y8eZPbt28bCydduXKFP//8kxo1ahjlra2tqVq1qvHOHjp0iFu3btGoUSOzdm/fvk3lypWz7EvhwoVxdHQ0AqZp+3bt2vVQ9Xp7exv3J6fzwI4ePZphw4aZ7fvw46F8NCQ8R+eLiIiIiKSnjyksR0FTEZEnQFrQzsbGhiJFimBjYwPczfZr3LgxjRs3Zv78+Xh6enLq1CmCg4O5ffu2cf6bb75Jx44dmTx5MhEREYSGhhpBzTRpdQJGNmNm+9ICXCkpKQwbNow2bdpk6G/6rMz0daTVk1ZHTk2cOJHJkyczZcoUypcvj5OTE3369DG7xvu1ldnAipzO0+Pk5ESpUqUy7LeysspQb9q0CY+yX1WqVOHEiRP8+OOPrF+/npCQEBo2bGg2J2dWunTpQs2aNTl48KCRNXyvsLAw/vrrL6ZMmcIzzzyDnZ0dQUFBubq/ixYt4v3332fixIkEBQXh4uLC+PHjM0yhcO+1pb/+tLp++OEHihYtalbu3kzNe9/N7Pr2MPWmPz8nBg8eTN++fc32pVory1RERERE5EmjoKmIyBMgq6Dd4cOHOX/+PGPGjMHHxwfAmH80vWbNmuHk5MSMGTP48ccf+emnnx66T1WqVOHIkSOZ9iun0ubWTE5Ozrbcli1baNWqFR06dADuBrGOHTtG2bJlc9xWQEAAp06d4o8//qBIkSIAbN++/QF7fpenpycHDx402xcXF5chgPco+uXq6kpoaCihoaG8+uqrNGnShIsXL1KgQIFs6y9XrhzlypVj//79tGvXLtMyW7Zs4fPPP6dZs2YAnD59mvPnz+f4GtLqqFWrFj169DD2pV84zM3Nzcj+fOGFF4C7z/3nn382slEDAgKws7Pj1KlT1K1bN1ftZ+dR1Wtra3vfd9XOLuNQ/Ft3sigsIiIiInIfWo/JchQ0FRF5ghUvXhxbW1umTZtG9+7dOXjwoDHnZHppK70PHjyYUqVK5XhYenaGDBlCixYt8PHx4bXXXsPKyor9+/dz4MABRo4cmaM6nnnmGUwmEytXrqRZs2Y4ODjg7OycoVypUqVYsmQJMTExuLu7M2nSJM6ePZuroGnDhg3x9/enU6dOTJw4katXr/Lhhx/m+PzMNGjQgPHjx/Pll18SFBTE/PnzOXjwYIYh3w/br8mTJ+Pt7U2lSpWwsrJi8eLFeHl55Xge2I0bN5KUlJRl+VKlSjFv3jyqVavG1atXGTBgAA4ODjm+hrQ6vvzyS9asWUOJEiWYN28esbGxlChRwijTq1cvRo8eTalSpShTpgzTpk3j0qVLRkani4sL/fv35/333yclJYXnn3+eq1evEhMTg7OzszEHb249qnp9fX05ceIEcXFxFCtWDBcXF81VKiIiIiLyL2Vl6Q6IiMiD8/T0JDIyksWLFxMQEMCYMWOMhZHu1bVrV27fvp3lEO3cCg4OZuXKlaxbt47q1avz3HPPMWnSJJ555pkc11G0aFGGDRvGBx98QOHChenZs2em5T7++GOqVKlCcHAw9erVw8vLK8uFm7JiZWXFsmXLSExMpEaNGrz55ptm87E+iODgYD7++GMGDhxI9erV+fvvv+nUqdMj75ezszNjx46lWrVqVK9enZMnT7Jq1SqsrHL2v3EnJ6dsA6xz587l0qVLVK5cmY4dO9K7d28KFSqUq+vo3r07bdq0ITQ0lJo1a3LhwgWzrFOAQYMG0bZtWzp16kRQUBDOzs4EBwebTecwYsQIhgwZwujRoylbtizBwcF8//33ZsHXB/Eo6n3llVdo0qQJ9evXx9PTk6+//vqh+iQiIiIiInmXKTWzydRERORfZ9u2bdSrV48zZ84YCz2JWFJKSgply5YlJCQk0wzpfwsNzxcReXLkxb+OU/JYp6ytNFZY/n3s8/A47O8P/GmxtluWf7r/bszDr4WIiDwKiYmJnD59mo8//piQkBAFTMVifvvtN9auXUvdunVJTExk+vTpnDhxIsu5VkVERERERCxFw/NFRP7lvv76a/z9/bly5Qrjxo2zdHfkKWZlZUVkZCTVq1endu3aHDhwgPXr1+dqbloRERERkaeJyWS57Wmn4fkiIiIij5GG54uIPDny4l/HGp4v8vjl5eH5Kw9abnh+i8Cne5SiMk1FRERERERERERE0snDsXQREREREREREZGnlwlld1uKMk1FRERERERERERE0lGmqYiIiIiIiIiISB6kBZksR5mmIiIiIiIiIiIiIuko01RERERERERERCQPstKcphajTFMRERERERERERGRdBQ0FREREREREREREUlHw/NFRERERERERETyIC0EZTkKmoqIiIiIiIiQN4MT1nmxUyIiTwEFTUVERERERERERPIgfW5iOZrTVERERERERERERCQdBU1FRERERERERERE0tHwfBERERERERERkTzIhMbnW4oyTUVERERERERERETSUaapiIiIiIiIiIhIHmSlRFOLUaapiIiIiIiIiIiISDrKNBUREREREREREcmDNKep5SjTVERERERERERERCQdBU1FRERERERERERE0tHwfBERERERERERkTzIpNH5FqNMU3mqhIeHU6lSpWzLhIWF0bp163+kPw/DZDKxfPnybMs8KdfytMvJs8wL/ql+njx5EpPJRFxc3GNtRz8fGUVGRpI/f/6HqsPX15cpU6Y8kv5k50n5uRERERERkSeTgqaSJ4WFhWEymTCZTNjY2FCyZEn69+/P9evXLd21PCMhIYGmTZsC/1yQSTIXHR2Nr6/vA5+f/lnmBVl9uPA4+plZ4NLHx4eEhAQCAwMfaVv3+vTTT4mMjMzVOf/2QF1oaChHjx7NUdmsAqyxsbG89dZbj7hnGeW1nxsRERERkcfBZMH/ciM8PNyI46RtXl5exvHU1FTCw8MpUqQIDg4O1KtXj19++cWsjsTERHr16kXBggVxcnLipZde4syZM2ZlLl26RMeOHXFzc8PNzY2OHTty+fLlB76/2VHQVPKsJk2akJCQwP/+9z9GjhzJ559/Tv/+/TMtm5SU9A/3zvK8vLyws7OzdDfkEXhSnuU/1U9ra2u8vLzIl+/xziDj5ub20FmV/zYODg4UKlTooerw9PTE0dHxEfUoa0/Kz42IiIiIyNOiXLlyJCQkGNuBAweMY+PGjWPSpElMnz6d2NhYvLy8aNSoEX///bdRpk+fPixbtoyFCxeydetWrl27RosWLUhOTjbKtGvXjri4OFavXs3q1auJi4ujY8eOj+V6FDSVPMvOzg4vLy98fHxo164d7du3NzK80jLh5s6dS8mSJbGzsyM1NZVTp07RqlUrnJ2dcXV1JSQkhD///DND3bNmzcLHxwdHR0dee+21bD+VSE1NZdy4cZQsWRIHBwcqVqzIt99+axyPjo7GZDKxZs0aKleujIODAw0aNODcuXP8+OOPlC1bFldXV9q2bcuNGzeybMPT05MlS5YY+ypVqmQWvNi+fTs2NjZcu3YNMM94K1GiBACVK1fGZDJRr149s/onTJiAt7c3Hh4evPvuu9kGmY8fP06rVq0oXLgwzs7OVK9enfXr12dZPs2KFSuoVq0a9vb2FCxYkDZt2hjHLl26RKdOnXB3d8fR0ZGmTZty7Ngx43haxtrKlSvx9/fH0dGRV199levXrxMVFYWvry/u7u706tXL7Jelr68vI0aMoF27djg7O1OkSBGmTZtm1q9JkyZRvnx5nJyc8PHxoUePHsY9TN/2mjVrKFu2LM7OzkbAHuCnn37CxsaGs2fPmtXbr18/6tSpk+m92LdvH/Xr18fFxQVXV1eqVq3K7t27s7x36Z9lWtbw0qVLqV+/Po6OjlSsWJHt27cDcOXKFRwcHFi9erVZHUuXLsXJycm4tt9//53Q0FDc3d3x8PCgVatWnDx50igfHR1NjRo1cHJyIn/+/NSuXZvffvuNyMhIhg0bxr59+4xPB9OyMe/NsoyJiaFSpUrY29tTrVo1li9fbpbxnJycTNeuXSlRogQODg74+/vz6aefGueHh4cTFRXFd999Z7QVHR2daeb05s2bqVGjBnZ2dnh7e/PBBx9w584d43i9evXo3bs3AwcOpECBAnh5eREeHp7lPYeMWa73qyMtm/jll1/GZDKZZRfPmDGDZ599FltbW/z9/Zk3b162bcfGxtKoUSMKFiyIm5sbdevWZe/evWZlTCYTX3zxBS+//DKOjo6ULl2aFStWZFvv7du3GThwIEWLFsXJyYmaNWsSHR0NwK1btyhXrpxZFuiJEydwc3Nj9uzZQMbs0aze5ejoaN544w2uXLliPLu0e3Xv8PycXMeKFSsoXbo0Dg4O1K9fn6ioKEwmU7a/m9O/j7dv36Znz554e3tjb2+Pr68vo0ePNsqGh4dTvHhx7OzsKFKkCL179860njT58+c3y0K+38+TiIiIiIhAvnz58PLyMjZPT0/gbsxjypQpfPjhh7Rp04bAwECioqK4ceMGX331FXD3b905c+YwceJEGjZsSOXKlZk/fz4HDhwwYhLx8fGsXr2aL774gqCgIIKCgpg9ezYrV67kyJEjj/x6FDSVJ4aDg4NZsO/XX39l0aJFLFmyxAiutG7dmosXL7J582bWrVvH8ePHCQ0NNasn7bzvv//e+FTi3XffzbLdjz76iIiICGbMmMEvv/zC+++/T4cOHdi8ebNZufDwcKZPn05MTAynT58mJCSEKVOm8NVXX/HDDz+wbt26DAG9NCaTiTp16hjBjUuXLnHo0CGSkpI4dOgQcDfIVbVqVZydnTOcv2vXLgDWr19PQkICS5cuNY5t2rSJ48ePs2nTJqKiooiMjMx2SPK1a9do1qwZ69ev5+effyY4OJiWLVty6tSpLM/54YcfaNOmDc2bN+fnn39mw4YNVKtWzTgeFhbG7t27WbFiBdu3byc1NZVmzZqZPc8bN24wdepUFi5cyOrVq4mOjqZNmzasWrWKVatWMW/ePP773/+aBawBxo8fT4UKFdi7dy+DBw/m/fffZ926dcZxKysrpk6dysGDB4mKimLjxo0MHDjQrI4bN24wYcIE5s2bx08//cSpU6eMrOY6depQsmRJsyDYnTt3mD9/Pm+88Uam96N9+/YUK1aM2NhY9uzZwwcffICNjU2W9y8zH374If379ycuLg4/Pz/atm3LnTt3cHNzo3nz5ixYsMCs/FdffWV8YHDjxg3q16+Ps7MzP/30E1u3bjWCwbdv3+bOnTu0bt2aunXrsn//frZv385bb72FyWQiNDSUfv36mX1CeO/PEMDff/9Ny5YtKV++PHv37mXEiBEMGjTIrExKSgrFihVj0aJFHDp0iCFDhvCf//yHRYsWAdC/f39CQkKMIHVCQgK1atXK0Nbvv/9Os2bNqF69Ovv27WPGjBnMmTOHkSNHmpWLiorCycmJnTt3Mm7cOIYPH272LuREdnXExsYCEBERQUJCgvH9smXLeO+99+jXrx8HDx7k7bff5o033mDTpk1ZtvP333/TuXNntmzZwo4dOyhdujTNmjUz+5QVYNiwYYSEhLB//36aNWtG+/btuXjxYpb1vvHGG2zbto2FCxeyf/9+XnvtNZo0acKxY8ewt7dnwYIFREVFsXz5cpKTk+nYsSP169enW7dumdaX1btcq1YtpkyZgqurq/HsshoJcL/rOHnyJK+++iqtW7cmLi6Ot99+mw8//DDLujIzdepUVqxYwaJFizhy5Ajz5883gtrffvstkydPZtasWRw7dozly5dTvnz5HNd9v58nEREREZHHycpkuS0xMZGrV6+abYmJiVn29dixYxQpUoQSJUrw+uuv87///Q+4m6xx9uxZGjdubJS1s7Ojbt26xMTEALBnzx6SkpLMyhQpUoTAwECjzPbt23Fzc6NmzZpGmeeeew43NzejzKP0eMc+ijwiu3bt4quvvuLFF1809t2+fZt58+YZn1ysW7eO/fv3c+LECXx8fACYN28e5cqVIzY2lurVqwN3s62ioqIoVqwYANOmTaN58+ZMnDjRbL4NgOvXrzNp0iQ2btxIUFAQACVLlmTr1q3MmjWLunXrGmVHjhxJ7dq1AejatSuDBw/m+PHjlCxZEoBXX32VTZs2ZQgspalXrx7//e9/gbvZjRUrVqR48eJER0cTEBBAdHR0hgzSNGn3wMPDI8M1uLu7M336dKytrSlTpgzNmzdnw4YNWQZJKlasSMWKFc2ua9myZaxYsYKePXtmes6oUaN4/fXXGTZsmFk9cPeX5ooVK9i2bZsREFuwYAE+Pj4sX76c1157Dbg7xUJatl7a/Zo3bx5//vknzs7OBAQEUL9+fTZt2mQWxKtduzYffPABAH5+fmzbto3JkyfTqFEj4G56f5oSJUowYsQI3nnnHT7//HNjf1JSEjNnzjTa7tmzJ8OHDzeOd+3alYiICAYMGADcDRLfuHGDkJAQ4O6zS591durUKQYMGECZMmUAKF26dKb3LTv9+/enefPmwN2AU7ly5fj1118pU6YM7du3p1OnTty4cQNHR0euXr3KDz/8YGQqL1y4ECsrK7744gtM/3+pxYiICPLnz090dDTVqlXjypUrtGjRwrjmsmXLGm07OzsbnxBmZcGCBZhMJmbPno29vT0BAQH8/vvvZu+VjY2N2TtRokQJYmJiWLRoESEhITg7O+Pg4EBiYmK2bX3++ef4+Pgwffp0TCYTZcqU4Y8//mDQoEEMGTIEK6u7n/9VqFCBoUOHAnfv+fTp09mwYYPxLuREdnWk/Zzlz5/frL8TJkwgLCyMHj16ANC3b1927NjBhAkTqF+/fqbtNGjQwOz7WbNm4e7uzubNm2nRooWxPywsjLZt2wLwySefMG3aNHbt2kWTJk0y1Hn8+HG+/vprzpw5Q5EiRYC779Hq1auJiIjgk08+oVKlSowcOZJu3brRtm1bjh8/nu0crdm9y25ubhnmKcpKdtcxc+ZM/P39GT9+PAD+/v4cPHiQUaNG3bfe9P0sXbo0zz//PCaTiWeeecbsmJeXFw0bNsTGxobixYtTo0aNHNd9v5+n9P+oExERERH5Nxk9erTZ33QAQ4cOzXRUX82aNfnyyy/x8/Pjzz//ZOTIkdSqVYtffvnFGLlZuHBhs3MKFy7Mb7/9BsDZs2extbXF3d09Q5m088+ePZvpdGKFChXKMDr0UVCmqeRZK1euxNnZGXt7e4KCgqhTp45ZpuYzzzxjBDHgbpq2j4+PETAFCAgIIH/+/MTHxxv7ihcvbgRMAYKCgkhJSck0lfvQoUPcunWLRo0a4ezsbGxffvklx48fNytboUIF4+vChQvj6OhoBEzT9p07dy7L602bBPn8+fNs3ryZevXqUa9ePTZv3sydO3eIiYkxC9LmVLly5bC2tja+9/b2zrYf169fZ+DAgca9c3Z25vDhw9lmmsbFxZkFtNOLj48nX758Zp8EeXh44O/vb/ZcHB0djQAe3L1fvr6+Zpm1md3DtGB2+u/T17tp0yYaNWpE0aJFcXFxoVOnTly4cMFsUbF72773HoWFhfHrr7+yY8cOAObOnUtISAhOTk6ZXnPfvn158803adiwIWPGjMnwruRE+vfJ29sbwOhT8+bNyZcvnzHEecmSJbi4uBjBmz179vDrr7/i4uJivLMFChTg1q1bHD9+nAIFChAWFmZkEX/66afGdAQ5deTIESpUqIC9vb2xL7NA1MyZM6lWrRqenp44Ozsze/bsbN+lzMTHxxMUFGQErOBusPzatWtmk4Knv2dw/3c9Mw9SR3x8vPGBSfr+pX8P73Xu3Dm6d++On5+fMYH5tWvXMtyb9P1xcnLCxcUly/7s3buX1NRU/Pz8zH5fbd682ewd7NevH/7+/kybNo2IiAgKFiyYZT8fxbt8v+s4cuSI8aFWmtwENeHuz2hcXBz+/v707t2btWvXGsdee+01bt68ScmSJenWrRvLli0zm9rhfu7383Sv3H4aLyIiIiKSHUsuBDV48GCuXLlitg0ePDjTfjZt2pRXXnmF8uXL07BhQ3744Qfg7mg+41rS/U0Hd4ft37vvXveWyax8Tup5EAqaSp5Vv3594uLiOHLkCLdu3WLp0qVmnyjcG7DK6ofkfj88accyK5OSkgLczSyMi4sztkOHDmUYJp5++LXJZMowHNtkMhn1ZSYwMBAPDw82b95sBE3r1q3L5s2biY2N5ebNmzz//PNZnp+V3PZjwIABLFmyhFGjRrFlyxbi4uIoX758tsNQHRwcsjyWmpqa5f709zyzfua27+nLAfz22280a9aMwMBAlixZwp49e/jss88A88XDMmsnfb8LFSpEy5YtiYiI4Ny5c6xatYouXbpk2X54eDi//PILzZs3Z+PGjQQEBLBs2bL79ju9e98n+L/30dbWlldffdWY++Wrr74iNDTUWDgpJSWFqlWrmr2zcXFxHD16lHbt2gF3M+W2b99OrVq1+Oabb/Dz8zOCwjmR2c/Vvc960aJFvP/++3Tp0oW1a9cSFxfHG2+8keshzdm1db93KCfvS3oP+85l1+f0wsLC2LNnD1OmTCEmJoa4uDg8PDwy3Jvc9CclJQVra2v27Nlj9tzj4+PN5pI9d+4cR44cwdra2mxu4cw8inf5fteRk3fpfqpUqcKJEycYMWIEN2/eJCQkhFdffRUAHx8fjhw5wmeffYaDgwM9evSgTp06xu+Ae3/ewfz3Q05+ntIbPXq0EQhP28aPHZ2hnIiIiIhIXmdnZ4erq6vZltPFWJ2cnChfvjzHjh0zRqfdmw167tw5I/vUy8uL27dvc+nSpWzLZLZuzV9//ZUhi/VRUNBU8iwnJydKlSrFM888k6P5IAMCAjh16hSnT5829h06dIgrV66YDT0+deoUf/zxh/H99u3bsbKyws/PL9M67ezsOHXqFKVKlTLb0me0Pgpp85p+9913HDx4kBdeeIHy5csbQ8erVKmCi4tLpufa2toCmC2S9KC2bNlCWFgYL7/8MuXLl8fLy+u+C55UqFCBDRs2ZHosICCAO3fusHPnTmPfhQsXOHr0qNlzeVD3Bvp27NhhDCXevXs3d+7cYeLEiTz33HP4+fmZPfvcePPNN1m4cCGzZs3i2WefzZBZeC8/Pz/ef/991q5dS5s2bYiIiHigdrPSvn17Vq9ezS+//MKmTZto3769caxKlSocO3aMQoUKZXhv3dzcjHKVK1dm8ODBxMTEEBgYaARhbW1t7/sulSlThv3795tl0N272NWWLVuoVasWPXr0oHLlypQqVSpDZl5O2goICCAmJsYssBUTE4OLiwtFixbN9txHzcbGJkN/y5Yty9atW832xcTEZPt+b9myhd69e9OsWTPKlSuHnZ0d58+ff6i+Va5cmeTkZM6dO5fhuacfQt+lSxcCAwP58ssvGThwoDFvclayepdz8uxyokyZMsb8sGmyWzgtK66uroSGhjJ79my++eYblixZYsyb6uDgwEsvvcTUqVOJjo5m+/btxkqenp6eZpnWx44dM1u0L6c/T2ky+zR+wKDMP40XEREREbkfk8ly28NITEwkPj4eb29vSpQogZeXl9maE7dv32bz5s3GNH5Vq1bFxsbGrExCQgIHDx40ygQFBXHlyhVjXReAnTt3cuXKlUzXx3hYCprKv0bDhg2pUKEC7du3Z+/evezatYtOnTpRt25ds0WJ7O3t6dy5M/v27TMCFyEhIZnOy+fi4kL//v15//33iYqK4vjx4/z888989tlnZinmj0q9evX46quvqFChAq6urkYgdcGCBVnOZwp3MyHTVlT/888/uXLlygP3oVSpUixdupS4uDj27dtHu3bt7ptpN3ToUL7++muGDh1KfHw8Bw4cYNy4ccDdORBbtWpFt27d2Lp1K/v27aNDhw4ULVqUVq1aPXA/02zbto1x48Zx9OhRPvvsMxYvXsx7770HwLPPPsudO3eYNm0a//vf/5g3bx4zZ858oHaCg4Nxc3Nj5MiRWS4ABXDz5k169uxJdHQ0v/32G9u2bSM2NvaRBIjTq1u3LoULF6Z9+/b4+vry3HPPGcfat29PwYIFadWqFVu2bOHEiRNs3ryZ9957jzNnznDixAkGDx7M9u3b+e2331i7dq1ZENvX15cTJ04QFxfH+fPnMx1anPZevPXWW8THx7NmzRomTJgA/F/WZalSpdi9ezdr1qzh6NGjfPzxxxmCY76+vuzfv58jR45w/vx5swy/ND169OD06dP06tWLw4cP89133zF06FD69u1rzGf6T/H19WXDhg2cPXvW+AR0wIABREZGMnPmTI4dO8akSZNYunRptgsjlSpVinnz5hEfH8/OnTtp3759thnbOeHn52fMd7t06VJOnDhBbGwsY8eOZdWqVQB89tlnbN++nS+//JJ27drx6quv0r59+0yzf+/3Lvv6+nLt2jU2bNjA+fPnzQKNufH2229z+PBhBg0axNGjR1m0aJGxWF1Oh9hMnjyZhQsXcvjwYY4ePcrixYvx8vIif/78REZGMmfOHA4ePGj8HnBwcDDmPW3QoAHTp09n79697N69m+7du5t9UHe/n6d7Pcyn8SIiIiIiT6r+/fuzefNmTpw4wc6dO3n11Ve5evUqnTt3xmQy0adPHz755BOWLVvGwYMHCQsLw9HR0Ri95ebmRteuXenXrx8bNmzg559/pkOHDsZwf7ibsNKkSRO6devGjh072LFjB926daNFixb4+/s/8mtS0FT+NUwmE8uXL8fd3Z06derQsGFDSpYsyTfffGNWrlSpUrRp04ZmzZrRuHFjAgMDzRYFuteIESMYMmQIo0ePpmzZsgQHB/P9999TokSJR34N9evXJzk52SxAWrduXZKTk7OdzzRfvnxMnTqVWbNmUaRIkYcKRk6ePBl3d3dq1apFy5YtCQ4OpkqVKtmeU69ePRYvXsyKFSuoVKkSDRo0MMssjYiIoGrVqrRo0YKgoCBSU1NZtWpVrleUz0y/fv3Ys2cPlStXZsSIEUycOJHg4GAAKlWqxKRJkxg7diyBgYEsWLCA0aMfbJislZUVYWFhJCcn06lTpyzLWVtbc+HCBTp16oSfnx8hISE0bdo0w+TZD8tkMtG2bVv27dtnlmUKd+do/emnnyhevDht2rShbNmydOnShZs3b+Lq6oqjoyOHDx/mlVdewc/Pj7feeouePXvy9ttvA/DKK6/QpEkT6tevj6enJ19//XWG9l1dXfn++++Ji4ujUqVKfPjhhwwZMgTAmOe0e/futGnThtDQUGrWrMmFCxeMxZLSdOvWDX9/f2Pe023btmVoq2jRoqxatYpdu3ZRsWJFunfvTteuXfnoo48eyb3MjYkTJ7Ju3Tp8fHyoXLkyAK1bt+bTTz9l/PjxlCtXjlmzZhEREZHtBx1z587l0qVLVK5cmY4dO9K7d+9MJzTPrYiICDp16mTMW/rSSy+xc+dOfHx8OHz4MAMGDDAW1oK7QdTLly/z8ccfZ6jrfu9yrVq16N69O6GhoXh6ehoflORWiRIl+Pbbb1m6dCkVKlRgxowZfPjhhwA5DjY6OzszduxYqlWrRvXq1Tl58iSrVq3CysqK/PnzM3v2bGrXrm1kxX///fd4eHgAd5+pj48PderUoV27dvTv3x9HR0ej7vv9PImIiIiICJw5c4a2bdvi7+9PmzZtsLW1ZceOHUaywsCBA+nTpw89evSgWrVq/P7776xdu9ZsRO3kyZNp3bo1ISEh1K5dG0dHR77//nuzdVoWLFhA+fLlady4MY0bN6ZChQrMmzfvsVyTKTW3E4eJiOQRvr6+9OnThz59+vwj7XXr1o0///zTWIBJzC1YsIA33niDK1euPHTWpDzdRo0axcyZM82mW3mS3cr5ulMiIiIiYgH2+Szdg6xtO3bp/oUek9ql3e9f6F8sD78WIiJ5w5UrV4iNjWXBggV89913lu5OnvHll19SsmRJihYtyr59+xg0aBAhISEKmEquff7551SvXh0PDw+2bdvG+PHj6dmzp6W7JSIiIiIiTzEFTUVE7qNVq1bs2rWLt99+m0aNGlm6O3nG2bNnGTJkCGfPnsXb25vXXnuNUaNGWbpb8gQ6duwYI0eO5OLFixQvXpx+/foxeLAWTxIRERERsXrYFZnkgWl4voiIiMhjpOH5IiIiInlbXh6ev/3XyxZrO6hUfou1nRdoISgRERERERERERGRdPJwLF1EREREREREROTppcH5lqNMUxEREREREREREZF0lGkqIiIiIiIiIiKSFynV1GKUaSoiIiIiIiIiIiKSjjJNRURERERERERE8iCTUk0tRpmmIiIiIiIiIiIiIukoaCoiIiIiIiIiIiKSjobni4iIiIiIiIiI5EEmjc63GGWaioiIiIiIiIiIiKSjTFMREREREZHHICU11dJdMHMnOW/1B8A2n/J45OGdvXzL0l0wk886b73XBV1sLd0FeQhKNLWcvPWTLCIiIiIiIiIiImJhCpqKiIiIiIiIiIiIpKPh+SIiIiIiIiIiInmRxudbjDJNRURERERERERERNJRpqmIiIiIiIiIiEgeZFKqqcUo01REREREREREREQkHWWaioiIiIiIiIiI5EEmJZpajDJNRURERERERERERNJR0FREREREREREREQkHQ3PFxERERERERERyYM0Ot9ylGkqeU54eDiVKlXKtkxYWBitW7f+R/rzMEwmE8uXL8+2zJNyLU+7nDzLvOCf6ufJkycxmUzExcU91nb085FRZGQk+fPnf6g6fH19mTJlyiPpjyXk5P2Ljo7GZDJx+fLlf6xfIiIiIiLy76GgqTywsLAwTCYTJpMJGxsbSpYsSf/+/bl+/bqlu5ZnJCQk0LRpU+CfCzJJ5qKjo/H19X3g89M/y7wgqw8XHkc/Mwtc+vj4kJCQQGBg4CNt616ffvopkZGRuTrnSQlwP6jQ0FCOHj2ao7JZBVhjY2N56623HnHPRERERETkkTNZcHvKaXi+PJQmTZoQERFBUlISW7Zs4c033+T69evMmDEjQ9mkpCRsbGws0EvL8fLysnQX5BF5Up7lP9VPa2vrf6QtNze3x97Gk8bBwQEHB4eHqsPT0/MR9ebRexr/XyEiIiIiInmPMk3lodjZ2eHl5YWPjw/t2rWjffv2RoZXWibc3LlzKVmyJHZ2dqSmpnLq1ClatWqFs7Mzrq6uhISE8Oeff2aoe9asWfj4+ODo6Mhrr72W7RDL1NRUxo0bR8mSJXFwcKBixYp8++23xvG0YZpr1qyhcuXKODg40KBBA86dO8ePP/5I2bJlcXV1pW3btty4cSPLNjw9PVmyZImxr1KlShQqVMj4fvv27djY2HDt2jXAPOOtRIkSAFSuXBmTyUS9evXM6p8wYQLe3t54eHjw7rvvkpSUlOX1Hj9+nFatWlG4cGGcnZ2pXr0669evz7J8mhUrVlCtWjXs7e0pWLAgbdq0MY5dunSJTp064e7ujqOjI02bNuXYsWPG8bSMtZUrV+Lv74+joyOvvvoq169fJyoqCl9fX9zd3enVqxfJycnGeb6+vowYMYJ27drh7OxMkSJFmDZtmlm/Jk2aRPny5XFycsLHx4cePXoY9zB922vWrKFs2bI4OzvTpEkTEhISAPjpp5+wsbHh7NmzZvX269ePOnXqZHov9u3bR/369XFxccHV1ZWqVauye/fuLO9d+meZljW8dOlS6tevj6OjIxUrVmT79u0AXLlyBQcHB1avXm1Wx9KlS3FycjKu7ffffyc0NBR3d3c8PDxo1aoVJ0+eNMpHR0dTo0YNnJycyJ8/P7Vr1+a3334jMjKSYcOGsW/fPiPbOy0b894sy5iYGCpVqoS9vT3VqlVj+fLlZhnPycnJdO3alRIlSuDg4IC/vz+ffvqpcX54eDhRUVF89913RlvR0dGZZk5v3ryZGjVqYGdnh7e3Nx988AF37twxjterV4/evXszcOBAChQogJeXF+Hh4Vnec8iY5Xq/OtKyiV9++WVMJpNZdvGMGTN49tlnsbW1xd/fn3nz5mXbdmxsLI0aNaJgwYK4ublRt25d9u7da1bGZDLxxRdf8PLLL+Po6Ejp0qVZsWJFtvXevn2bgQMHUrRoUZycnKhZsybR0dEA3Lp1i3LlypllgZ44cQI3Nzdmz54NZMwezepdjo6O5o033uDKlSvGs0u7V/cOz8/JdaxYsYLSpUvj4OBA/fr1iYqKuu/wd5PJxIwZM2jatCkODg6UKFGCxYsXG8fT3qNFixZRr1497O3tmT9/PikpKQwfPpxixYphZ2dHpUqVMvw8ARw+fJhatWphb29PuXLljPuYlZiYGOrUqYODgwM+Pj707t3bbHSEr68vI0eOpFOnTjg7O/PMM8/w3Xff8ddffxn/3ypfvny2vytEREREROTfQUFTeaQcHBzMgn2//vorixYtYsmSJUZwpXXrGSSzHgABAABJREFU1ly8eJHNmzezbt06jh8/TmhoqFk9aed9//33rF69mri4ON59990s2/3oo4+IiIhgxowZ/PLLL7z//vt06NCBzZs3m5ULDw9n+vTpxMTEcPr0aUJCQpgyZQpfffUVP/zwA+vWrcsQ0EtjMpmoU6eO8Uf5pUuXOHToEElJSRw6dAi4G+SqWrUqzs7OGc7ftWsXAOvXrychIYGlS5caxzZt2sTx48fZtGkTUVFRREZGZjsk+dq1azRr1oz169fz888/ExwcTMuWLTl16lSW5/zwww+0adOG5s2b8/PPP7NhwwaqVatmHA8LC2P37t2sWLGC7du3k5qaSrNmzcye540bN5g6dSoLFy5k9erVREdH06ZNG1atWsWqVauYN28e//3vf80C1gDjx4+nQoX/x96dx9WY/v8Df532UpIoW0pSilAyM4UWW5YxWQsNshtL1hgfSwljGbuxDSMxxr6MfVdSIVFZsjVRQyY7adFy//7w6/52OqdkO+fg9ZzH/Xi4r/s+9/2+7/s6pznvcy0NcPHiRUyaNAljxozBsWPHxO1qampYunQprly5gtDQUJw8eRITJkyQOkZmZibmz5+PjRs34vTp00hJScH48eMBAK6urrC0tJRKguXl5eHPP/9Ev3795N4PX19f1KhRAzExMYiNjcXPP//8zq3bJk+ejPHjxyMuLg7W1tbo2bMn8vLyYGhoiA4dOmDTpk1S+//1119i4iUzMxMeHh7Q19fH6dOncebMGTEZ/Pr1a+Tl5aFTp05wc3NDQkICoqOjMXjwYEgkEvj4+GDcuHGoV68e0tLSkJaWJvMeAoCXL1+iY8eOsLe3x8WLFzFjxgxMnDhRap+CggLUqFED27Ztw7Vr1zBt2jT873//w7Zt2wAA48ePh7e3t5ikTktLg4uLi8y57t27h/bt26NJkyaIj4/HypUr8ccff2DmzJlS+4WGhqJcuXI4d+4c5s2bh+DgYKm6UBalHSMmJgYAEBISgrS0NHF99+7dGDVqFMaNG4crV65gyJAh6NevH06dOlXieV6+fIm+ffsiIiICZ8+eRZ06ddC+fXu8fPlSar/p06fD29sbCQkJaN++PXx9ffHkyZMSj9uvXz9ERkZiy5YtSEhIQPfu3dG2bVvcunULOjo62LRpE0JDQ7Fnzx7k5+ejd+/e8PDwwKBBg+Qer6S67OLigsWLF6N8+fLisyt8z8hT2nXcuXMH3bp1Q6dOnRAXF4chQ4Zg8uTJJR6rqKlTp6Jr166Ij4/Hjz/+iJ49eyIxMVFqn4kTJ8Lf3x+JiYnw9PTEkiVLsGDBAsyfPx8JCQnw9PTEDz/8IPVDDgAEBARg3LhxuHTpElxcXPDDDz/g8ePHcuO4fPkyPD090aVLFyQkJGDr1q04c+YMRowYIbXfokWL0LRpU1y6dAkdOnRA79690adPH/z444+4ePEirKys0KdPHwiCUKbrJyIiIiL6EBIl/vfVE4jeU9++fQUvLy9x/dy5c4KxsbHg7e0tCIIgBAYGCpqamkJ6erq4z9GjRwV1dXUhJSVFLLt69aoAQDh//rz4OnV1dSE1NVXc59ChQ4KampqQlpYmc+6MjAxBR0dHiIqKkopvwIABQs+ePQVBEIRTp04JAITjx4+L22fPni0AEJKSksSyIUOGCJ6eniVe89KlS4X69esLgiAIe/bsEZycnIQuXboIy5cvFwRBENq0aSNMnDhR3B+AsHv3bkEQBCE5OVkAIFy6dEnmPpqbmwt5eXliWffu3QUfH58S45DHzs5OWLZsWYnbnZ2dBV9fX7nbbt68KQAQIiMjxbJHjx4Jurq6wrZt2wRBEISQkBABgHD79m1xnyFDhgh6enrCy5cvxTJPT09hyJAh4rq5ubnQtm1bqfP5+PgI7dq1KzHWbdu2CcbGxuK6vHMvX75cMDU1Fdfnzp0r2Nraiut79uwR9PX1hYyMDLnnMDAwENavX19iDMXJe5Zr164VtxfW48TEREEQBGHXrl2Cvr6+8OrVK0EQBOH58+eCjo6OcODAAUEQBOGPP/4QbGxshIKCAvEYOTk5gq6urnDkyBHh8ePHAgAhLCxMbjyBgYFCw4YNS41z5cqVgrGxsZCVlSVuX7Nmjdx6WNSwYcOErl27iuvF3+tF70Hhcf73v//JXM/y5csFfX19IT8/XxAEQXBzcxOaNWsmdZwmTZpIvWeKK37ushyj6D0o5OLiIgwaNEiqrHv37kL79u1LPHdxeXl5goGBgbBv3z6pc02ZMkVcz8jIECQSiXDo0CG5x7h9+7YgkUiEe/fuSZW3bNlSmDRpkrg+b948oVKlSsLIkSOFKlWqCA8fPhS3hYSECIaGhuJ6aXW5+L6FzM3NhUWLFpX5OiZOnCh+9hWaPHmyAEB4+vSp3HMXHnfo0KFSZd9++63w008/CYLwf/Vo8eLFUvtUq1ZNmDVrllRZkyZNhGHDhkm9bs6cOeL23NxcoUaNGsLcuXMFQfi/z/3C+Hr37i0MHjxY6pgRERGCmpqa+B4xNzcXfvzxR3F7WlqaAECYOnWqWBYdHS0AEP8elUVWLhcuXL7W5dXrApVanmflq9yi7GfE5ctYkh9mqdSS+iRHpRZlP5/PYVFlF++8UNrytWNLU/og+/fvh76+PnR0dODs7AxXV1eplprm5uZSY+clJibCzMwMZmZmYpmdnR0qVKgg1fKoZs2aqFGjhrju7OyMgoIC3LhxQyaGa9euITs7G61bt4a+vr64bNiwAUlJSVL7NmjQQPy3qakp9PT0YGlpKVWWnp5e4vW6u7vj6tWrePToEcLDw+Hu7g53d3eEh4cjLy8PUVFRcHNze9ttk1GvXj2oq6uL61WrVi01jlevXmHChAnivdPX18f169dLbWkaFxeHli1byt2WmJgIDQ0NfPvtt2KZsbExbGxspJ6Lnp4eateuLa6bmprCwsJCqmWtvHvo7Owss170uKdOnULr1q1RvXp1GBgYoE+fPnj8+LFUt9ni5y5+j/z8/HD79m2cPXsWALBu3Tp4e3ujXLlycq957NixGDhwIFq1aoU5c+bI1JWyKFqfqlatCgBiTB06dICGhobYxXnnzp0wMDBAmzZtAACxsbG4ffs2DAwMxDpbsWJFZGdnIykpCRUrVoSfn5/YinjJkiXicARldePGDTRo0AA6Ojpi2TfffCOz36pVq+Dk5ITKlStDX18fa9asKbUuyZOYmAhnZ2dIJP/3a2TTpk2RkZGBf//9Vywres+At9d1ed7nGImJiWjatKlUWdOmTWVaPBaVnp6OoUOHwtraGoaGhjA0NERGRobMvSkaT7ly5WBgYFBiPBcvXoQgCLC2tpb6vAoPD5eqg+PGjYONjQ2WLVuGkJAQVKpUqcQ4P0Zdftt13LhxA02aNJHaX15dkudt738AUq3eX7x4gfv375fpeRU9toaGBpycnEp8prGxsVi/fr3Ufff09ERBQQGSk5PF/Yr/nQAAe3t7mbKSnnFOTg5evHghteTk5Mjdl4iIiIjobSQS5S1fOyZN6YN4eHggLi4ON27cQHZ2Nnbt2iU1xmfxhJUgCFJJlbeVFyrcJm+fgoICAG+6n8fFxYnLtWvXZLqJF+1+LZFIZLpjSyQS8Xjy1K9fH8bGxggPDxeTpm5ubggPD0dMTAyysrLQrFmzEl9fkneNIyAgADt37sSsWbMQERGBuLg42Nvb4/Xr1yW+prSJY4QSupkWfy7y4nzX2IvuBwB3795F+/btUb9+fezcuROxsbFYvnw5AEgNDSDvPEXjNjExQceOHRESEoL09HQcPHgQ/fv3L/H8QUFBuHr1Kjp06ICTJ0/Czs4Ou3fvfmvcRRWvT8D/1UctLS1069YNf/31F4A3XfN9fHygoaEh7te4cWOpOhsXF4ebN2+iV69eAN50MY+OjoaLiwu2bt0Ka2trMSlcFvLeV8Wf9bZt2zBmzBj0798fR48eRVxcHPr161dqXXrXc72tDpWlvhT1oXWutJiL8vPzQ2xsLBYvXoyoqCjExcXB2NhY5t68SzwFBQVQV1dHbGys1HNPTEyUGks2PT0dN27cgLq6ukyX9OI+Rl1+23WUpS69i+LHkvfjxrs+r5JeV6igoABDhgyRuu/x8fG4deuW1A8y8t7Xpb3Xi5s9e7aYZC9cfp07+61xExERERGRamHSlD5IuXLlYGVlBXNz8zKNB2lnZ4eUlBSkpqaKZdeuXcPz589ha2srlqWkpOD+/fvienR0NNTU1GBtbS33mNra2khJSYGVlZXUUrRF68dQOK7p33//jStXrqB58+awt7dHbm4uVq1aBUdHRxgYGMh9rZaWFgBITZL0viIiIuDn54fOnTvD3t4eVapUkZpASJ4GDRrgxIkTcrfZ2dkhLy8P586dE8seP36MmzdvSj2X91U80Xf27FnUrVsXAHDhwgXk5eVhwYIF+O6772BtbS317N/FwIEDsWXLFqxevRq1a9eWaalWnLW1NcaMGYOjR4+iS5cuCAkJea/zlsTX1xeHDx/G1atXcerUKfj6+orbHB0dcevWLZiYmMjU26Izxjs4OGDSpEmIiopC/fr1xSSslpbWW+tS3bp1kZCQINXKrfgENhEREXBxccGwYcPg4OAAKysrmZaKZTmXnZ0doqKipBJpUVFRMDAwQPXq1Ut97cemqakpE6+trS3OnDkjVRYVFVVq/Y6IiIC/vz/at2+PevXqQVtbG48ePfqg2BwcHJCfn4/09HSZ516lShVxv/79+6N+/frYsGEDJkyYII6bXJKS6nJZnl1Z1K1bVxwftlBZJ0Mq7f0vT/ny5VGtWrUyPa+ix87Ly0NsbGyJx3Z0dMTVq1dl7ruVlZX4+fwxTJo0Cc+fP5daAiZO+mjHJyIiIqKvi0SJy9eOSVNSqFatWqFBgwbw9fXFxYsXcf78efTp0wdubm5S3TN1dHTQt29fxMfHi4kLb29vqaRCIQMDA4wfPx5jxoxBaGgokpKScOnSJSxfvhyhoaEf/Rrc3d3x119/oUGDBihfvryYSN20aRPc3d1LfJ2JiYk4o/p///2H58+fv3cMVlZW2LVrl9hSqlevXm9taRcYGIjNmzcjMDAQiYmJuHz5MubNmwcAqFOnDry8vDBo0CCcOXNGnLClevXq8PLyeu84C0VGRmLevHm4efMmli9fju3bt2PUqFEAgNq1ayMvLw/Lli3DP//8g40bN2LVqlXvdR5PT08YGhpi5syZJU4ABQBZWVkYMWIEwsLCcPfuXURGRiImJuajJIiLcnNzg6mpKXx9fWFhYYHvvvtO3Obr64tKlSrBy8sLERERSE5ORnh4OEaNGoV///0XycnJmDRpEqKjo3H37l0cPXpUKoltYWGB5ORkxMXF4dGjR3K7/xbWi8GDByMxMRFHjhzB/PnzAfxfazkrKytcuHABR44cwc2bNzF16lSZ5JiFhQUSEhJw48YNPHr0SKoFcKFhw4YhNTUVI0eOxPXr1/H3338jMDAQY8eOhZqaYv/UWFhY4MSJE3jw4AGePn0K4E3r7PXr12PVqlW4desWFi5ciF27dpU6MZKVlRU2btyIxMREnDt3Dr6+vqW22C4La2tr+Pr6ok+fPti1axeSk5MRExODuXPn4uDBgwCA5cuXIzo6Ghs2bECvXr3QrVs3+Pr6ym39+7a6bGFhgYyMDJw4cQKPHj1CZmbme8U9ZMgQXL9+HRMnTsTNmzexbds2cbK6t7X+3L59O9atW4ebN28iMDAQ58+fl5l8qbiAgADMnTsXW7duxY0bN/Dzzz8jLi5O/NwotHz5cuzevRvXr1/H8OHD8fTp0xJbmE+cOBHR0dEYPnw44uLicOvWLezduxcjR44s+40oA21tbZQvX15q0dbW/qjnICIiIiKiT49JU1IoiUSCPXv2wMjICK6urmjVqhUsLS2xdetWqf2srKzQpUsXtG/fHm3atEH9+vWxYsWKEo87Y8YMTJs2DbNnz4atrS08PT2xb98+1KpV66Nfg4eHB/Lz86USpG5ubsjPzy91PFMNDQ0sXboUq1evRrVq1T4oGblo0SIYGRnBxcUFHTt2hKenJxwdHUt9jbu7O7Zv3469e/eiUaNGaNGihVTL0pCQEDRu3Bjff/89nJ2dIQgCDh48+M4zysszbtw4xMbGwsHBATNmzMCCBQvg6ekJAGjUqBEWLlyIuXPnon79+ti0aRNmz36/rqxqamrw8/NDfn4++vTpU+J+6urqePz4Mfr06QNra2t4e3ujXbt2mD59+nudtyQSiQQ9e/ZEfHy8VCtT4M0YradPn0bNmjXRpUsX2Nraon///sjKykL58uWhp6eH69evo2vXrrC2tsbgwYMxYsQIDBkyBADQtWtXtG3bFh4eHqhcuTI2b94sc/7y5ctj3759iIuLQ6NGjTB58mRMmzYNAMRxTocOHYouXbrAx8cH3377LR4/foxhw4ZJHWfQoEGwsbERxz2NjIyUOVf16tVx8OBBnD9/Hg0bNsTQoUMxYMAATJky5aPcy3exYMECHDt2DGZmZnBwcAAAdOrUCUuWLMGvv/6KevXqYfXq1QgJCSn1h45169bh6dOncHBwQO/eveHv7y81/Mj7CgkJQZ8+fcRxS3/44QecO3cOZmZmuH79OgICArBixQqxpfzy5cvx7NkzTJ06VeZYb6vLLi4uGDp0KHx8fFC5cmXxh5J3VatWLezYsQO7du1CgwYNsHLlSkyePBkA3poQnD59OrZs2YIGDRogNDQUmzZtgp2dXamv8ff3x7hx4zBu3DjY29vj8OHD2Lt3L+rUqSO135w5czB37lw0bNgQERER+Pvvv0sc/7VBgwYIDw/HrVu30Lx5czg4OGDq1KnieMRERERERERFSYQPGZSMiOgtLCwsMHr0aIwePVoh5xs0aBD+++8/cQImkrZp0yb069cPz58//+BWk/R1mzVrFlatWiU13EpxEokEu3fvRqdOnRQXmArKzlN2BESkLAUq9lUrL1+14gEALQ2246EP9+BZtrJDkKKhrlr1upLBxxuK6Eulo6HsCEoWn/pSaeduaCZ/+MGvhQpXCyKisnv+/DliYmKwadMm/P3338oOR2Vs2LABlpaWqF69OuLj4zFx4kR4e3szYUrvbMWKFWjSpAmMjY0RGRmJX3/99a3d7ImIiIiIiD5XTJoS0RfBy8sL58+fx5AhQ9C6dWtlh6MyHjx4gGnTpuHBgweoWrUqunfvjlmzZik7LPoM3bp1CzNnzsSTJ09Qs2ZNjBs3DpMmcYIjIiIiIqJPScIpmZSG3fOJiIiIPiF2zyf6erF7/tuxez59DOyeXzp2z387Ve6en5CaobRzNzDTV9q5VYFqvZOJiIiIiIiIiIiIlEyFc+lERERERERERERfLwl75ysNW5oSERERERERERERFcGWpkRERERERERERCqIDU2Vhy1NiYiIiIiIiIiIiIpg0pSIiIiIiIiIiIioCHbPJyIiIiIiIiIiUkXsn680bGlKREREREREREREVARbmhIREREREREREakgCZuaKg1bmhIREREREREREREVwZamREREREREn0BunqDsEKRkvs5TdggytDS0lB0CfQEM9TSVHYKUldF3lB2ClLFutZUdAn0ACRuaKg1bmhIREREREREREREVwaQpERERERERERERURHsnk9ERERERERERKSC2DtfedjSlIiIiIiIiIiIiKgItjQlIiIiIiIiIiJSRWxqqjRsaUpERERERERERERUBJOmREREREREREREREWwez4REREREREREZEKkrB/vtKwpSkRERERERERERFREWxpSkREREREREREpIIkbGiqNF9kS9OgoCA0atSo1H38/PzQqVMnhcTzISQSCfbs2VPqPp/LtbwLCwsLLF68WFwvy334nH2Jz/BLExYWBolEgmfPnik7lFIpMs7169ejQoUKn/w8xT8P6MM/M+7cuQOJRIK4uLiPFpOilaX+leX/B4iIiIiIiORRatLUz88PEokEEokEmpqasLS0xPjx4/Hq1StlhqVS0tLS0K5dOwBfxpfc9/Wl3IeSYl+yZAnWr1+vlJi+JkFBQfDz83uv17q4uCAtLQ2GhoYfN6gP4O7ujtGjR0uVfao45SUufXx8cPPmzY96HnliYmIwePDgMu//uSS4P8S7fGbIS7CamZkhLS0N9evX//jBERERERHRRyNR4vK1U3r3/LZt2yIkJAS5ubmIiIjAwIED8erVK6xcuVJm39zcXGhqaiohSuWpUqWKskN4b69fv4aWltZHOdbHuA+CICA/Px8aGsqp9q9fvy5xmyol4kg+LS2tz+L9qMg4dXV1oaur+8nPU7ly5U9+js/Nh35mqKurq2x9zs/Ph0QigZraF9kZhoiIiIiIPhNK/0aira2NKlWqwMzMDL169YKvr6/YDbuwW926detgaWkJbW1tCIKAlJQUeHl5QV9fH+XLl4e3tzf+++8/mWOvXr0aZmZm0NPTQ/fu3UttdSQIAubNmwdLS0vo6uqiYcOG2LFjh7i9sOXSkSNH4ODgAF1dXbRo0QLp6ek4dOgQbG1tUb58efTs2ROZmZklnqNy5crYuXOnWNaoUSOYmJiI69HR0dDU1ERGRgYA6W7ptWrVAgA4ODhAIpHA3d1d6vjz589H1apVYWxsjOHDhyM3N7fE601KSoKXlxdMTU2hr6+PJk2a4Pjx4yXuX2jv3r1wcnKCjo4OKlWqhC5duojbLCwsMHPmTPj5+cHQ0BCDBg0CAERFRcHV1RW6urowMzODv7+/VGvi9PR0dOzYEbq6uqhVqxY2bdokc953uQ+Fij4zJycnaGtrIyIioszP+sCBA2jYsCF0dHTw7bff4vLly1LH37lzJ+rVqwdtbW1YWFhgwYIFUtvl3Y+SYi/eEszd3R3+/v6YMGECKlasiCpVqiAoKEjq+NevX0ezZs2go6MDOzs7HD9+/K3DGBQUFGDu3LmwsrKCtrY2atasiVmzZonbL1++jBYtWkBXVxfGxsYYPHiwWBeLxvnLL7/A1NQUFSpUwPTp05GXl4eAgABUrFgRNWrUwLp168TXFLau3bJlC1xcXKCjo4N69eohLCxM3Cc/Px8DBgxArVq1oKurCxsbGyxZskQq9sJzl1TPg4ODYW9vL3PNjRs3xrRp0+Tejx07dsDe3l683latWpXY0r1468XCrsFHjhyBra0t9PX10bZtW6SlpQEAjhw5Ah0dHZnPHX9/f7i5uYnrb3t/rFixAnXq1IGOjg5MTU3RrVs38X6Eh4djyZIlYov9O3fuyG1luWbNGvGzsHPnzli4cKFUt+a3fR64u7vj7t27GDNmjHiuovegqJUrV6J27drQ0tKCjY0NNm7cKLVdIpFg7dq16Ny5M/T09FCnTh3s3btX7j0vJG+4jpKOcefOHXh4eAAAjIyMIJFIxNbFOTk58Pf3h4mJCXR0dNCsWTPExMSUeu4///wTTk5OMDAwQJUqVdCrVy+kp6eL2wvv94kTJ+Dk5AQ9PT24uLjgxo0bpR733r178PHxgZGREYyNjeHl5YU7d+4AePPe1tPTw19//SXuv2vXLujo6IifQ8U/M0qqy0FBQQgNDcXff/8tPruwsDCZVu9lvY6ZM2fCxMQEBgYGGDhwIH7++edSu7+X5fO0sB7t378fdnZ20NbWxt27d/H06VP06dMHRkZG0NPTQ7t27XDr1i2Zc+zZswfW1tbQ0dFB69atkZqaWuq9DwkJga2tLXR0dFC3bl2sWLFC3FZ4X7Zt24bmzZtDV1cXTZo0wc2bNxETEwMnJyfxvf7w4cNSz0NERERERJ83pSdNi9PV1ZVK9t2+fRvbtm3Dzp07xS93nTp1wpMnTxAeHo5jx44hKSkJPj4+UscpfN2+fftw+PBhxMXFYfjw4SWed8qUKQgJCcHKlStx9epVjBkzBj/++CPCw8Ol9gsKCsJvv/2GqKgopKamwtvbG4sXL8Zff/2FAwcO4NixY1i2bJncc0gkEri6uorJoqdPn+LatWvIzc3FtWvXALz5gtm4cWPo6+vLvP78+fMAgOPHjyMtLQ27du0St506dQpJSUk4deoUQkNDsX79+lK7bmZkZKB9+/Y4fvw4Ll26BE9PT3Ts2BEpKSklvubAgQPo0qULOnTogEuXLolfrov69ddfUb9+fcTGxmLq1Km4fPkyPD090aVLFyQkJGDr1q04c+YMRowYIb7Gz88Pd+7cwcmTJ7Fjxw6sWLFCKinxLvdBngkTJmD27NlITExEgwYNyvysAwICMH/+fMTExMDExAQ//PCDWDdjY2Ph7e2NHj164PLlywgKCsLUqVNl7nnx+/EusYeGhqJcuXI4d+4c5s2bh+DgYBw7dgzAm+Rnp06doKenh3PnzuH333/H5MmTS70PADBp0iTMnTsXU6dOxbVr1/DXX3/B1NQUAJCZmYm2bdvCyMgIMTEx2L59O44fPy71rADg5MmTuH//Pk6fPo2FCxciKCgI33//PYyMjHDu3DkMHToUQ4cOlUlcBAQEYNy4cbh06RJcXFzwww8/4PHjx+L11KhRA9u2bcO1a9cwbdo0/O9//8O2bdukjlFaPe/fvz+uXbsmlQRLSEjApUuX5HbJT0tLQ8+ePdG/f38kJiYiLCwMXbp0gSAIb72PhTIzMzF//nxs3LgRp0+fRkpKCsaPHw8AaNWqFSpUqCD1I0l+fj62bdsGX19fAHjr++PChQvw9/dHcHAwbty4gcOHD8PV1RXAm+7Zzs7OGDRoENLS0pCWlgYzMzOZGCMjIzF06FCMGjUKcXFxaN26tVSiHHj758GuXbtQo0YNBAcHi+eSZ/fu3Rg1ahTGjRuHK1euYMiQIejXrx9OnToltd/06dPh7e2NhIQEtG/fHr6+vnjy5EmZ73tpxzAzMxPv+Y0bN5CWliYm4CdMmICdO3ciNDQUFy9ehJWVFTw9PUs99+vXrzFjxgzEx8djz549SE5OllufJk+ejAULFuDChQvQ0NBA//79SzxmZmYmPDw8oK+vj9OnT+PMmTNiIu7169eoW7cu5s+fj2HDhuHu3bu4f/8+Bg0ahDlz5sj9YaC0ujx+/Hh4e3uLCf20tDS4uLiUGFtp17Fp0ybMmjULc+fORWxsLGrWrCm3V4g8pX2eFt6T2bNnY+3atbh69SpMTEzg5+eHCxcuYO/evYiOjoYgCGjfvr3M62bNmoXQ0FBERkbixYsX6NGjR4lxrFmzBpMnT8asWbOQmJiIX375BVOnTkVoaKjUfoGBgZgyZQouXrwIDQ0N9OzZExMmTMCSJUsQERGBpKSkEn+MISIiIiL6qNg/X3kEJerbt6/g5eUlrp87d04wNjYWvL29BUEQhMDAQEFTU1NIT08X9zl69Kigrq4upKSkiGVXr14VAAjnz58XX6euri6kpqaK+xw6dEhQU1MT0tLSZM6dkZEh6OjoCFFRUVLxDRgwQOjZs6cgCIJw6tQpAYBw/Phxcfvs2bMFAEJSUpJYNmTIEMHT07PEa166dKlQv359QRAEYc+ePYKTk5PQpUsXYfny5YIgCEKbNm2EiRMnivsDEHbv3i0IgiAkJycLAIRLly7J3Edzc3MhLy9PLOvevbvg4+NTYhzy2NnZCcuWLStxu7Ozs+Dr61vidnNzc6FTp05SZb179xYGDx4sVRYRESGoqakJWVlZwo0bNwQAwtmzZ8XtiYmJAgBh0aJFYllZ7kNxhc9sz549Ytm7POstW7aI2x8/fizo6uoKW7duFQRBEHr16iW0bt1a6hgBAQGCnZ1dqfejtGdY9L3g5uYmNGvWTGqfJk2aiHXj0KFDgoaGhlifBUEQjh07JnWfinvx4oWgra0trFmzRu7233//XTAyMhIyMjLEsgMHDghqamrCgwcPxDjNzc2F/Px8cR8bGxuhefPm4npeXp5Qrlw5YfPmzVLXPGfOHHGf3NxcoUaNGsLcuXPlxiIIgjBs2DCha9eu4npZ6nm7du2En376SVwfPXq04O7uLvf4sbGxAgDhzp07JcZQVGG9ePr0qSAIghASEiIAEG7fvi3us3z5csHU1FRc9/f3F1q0aCGuHzlyRNDS0hKePHkiCMLb3x87d+4UypcvL7x48UJuTG5ubsKoUaNKjdPHx0fo0KGD1D6+vr6CoaFhqddb/PPA3Nxc6j1ZeA+KHsfFxUUYNGiQ1D7du3cX2rdvL64DEKZMmSKuZ2RkCBKJRDh06FCJsRQ/99uOUfweFO6jqakpbNq0SSx7/fq1UK1aNWHevHklnru48+fPCwCEly9fSp2r6N+GAwcOCACErKwsucf4448/BBsbG6GgoEAsy8nJEXR1dYUjR46IZR06dBCaN28utGzZUmjdurXU/kU/M95Wl4t/vgiC7GdRWa7j22+/FYYPHy51nKZNmwoNGzaUe96ixy3t87TwvRQXFyfuc/PmTQGAEBkZKZY9evRI0NXVFbZt2yb1Onl/P86dOycIwpv/Hygan5mZmfDXX39JxThjxgzB2dlZ6r6sXbtW3L5582YBgHDixAmxbPbs2YKNjU2J111cVi4XLly+1uVZZr5KLfef5ajcouxnxOXLWJ68ylOpZdbx2yq1KPv5fA6LKrvx4JXSlq+d0lua7t+/H/r6+tDR0YGzszNcXV2lWmqam5tLjWeXmJgIMzMzqRZVdnZ2qFChAhITE8WymjVrokaNGuK6s7MzCgoK5HaZvHbtGrKzs9G6dWvo6+uLy4YNG5CUlCS1b4MGDcR/m5qaQk9PD5aWllJlpbWSdHd3x9WrV/Ho0SOEh4fD3d0d7u7uCA8PR15eHqKioqS67pZVvXr1oK6uLq5XrVq11DhevXqFCRMmiPdOX18f169fL7WlaVxcHFq2bFlqHMVbnsbGxmL9+vVS99XT0xMFBQVITk5GYmIiNDQ0pF5Xt27djzojd9Fjv8uzdnZ2Fv9dsWJF2NjYiHUsMTERTZs2ldq/adOmuHXrFvLz8+We+10VrWuA9DO9ceMGzMzMpMYk/Oabb0o9XmJiInJyckp8homJiWjYsCHKlSsnljVt2lTmfVOvXj2psQZNTU2lWr+pq6vD2NhYpv4VvZ+Fz7zoe3bVqlVwcnJC5cqVoa+vjzVr1sjUx7fV80GDBmHz5s3Izs5Gbm4uNm3aVGKLv4YNG6Jly5awt7dH9+7dsWbNGjx9+lTuviXR09ND7dq1S4zH19cXYWFhuH//PoA3LfXat28PIyMjAG9/f7Ru3Rrm5uawtLRE7969sWnTphKH/yjJjRs3ZOpG8fX3+TyQp6T3RdHnDEjX7XLlysHAwKDUzyt53vUYSUlJyM3NlYpPU1MT33zzjUx8RV26dAleXl4wNzeHgYGBOKRG8XtTNJ6qVasCQInxxMbG4vbt2zAwMBCfe8WKFZGdnS31ObRu3TokJCTg4sWLWL9+vTgsQnEfoy6X5TrKUpdKUtrnKfBmLN6i5y782/Dtt9+KZcbGxjKvK+nvh7xn+vDhQ6SmpmLAgAFS77mZM2e+9W89AKnPudL+1ufk5ODFixdSS05OTsk3h4iIiIioFBIl/ve1U/pEUB4eHli5ciU0NTVRrVo1mYmeiiZwgDfjgsr74lhSeaHCbfL2KSgoAPCm+3n16tWltmlra0utF41PIpHIxCuRSMTjyVO/fn0YGxsjPDwc4eHhCA4OhpmZGWbNmoWYmBhkZWWhWbNmJb6+JO8aR0BAAI4cOYL58+fDysoKurq66NatW6mTFZVlwpfiz6ugoABDhgyBv7+/zL41a9YUk3GlPbsPVTSmd3nW8hTGKa++CXK6dRe/H++itGf6tvouz9ueX2nHLFouL653rX/Fj7tt2zaMGTMGCxYsgLOzMwwMDPDrr7/i3LlzUvu/7TwdO3aEtrY2du/eDW1tbeTk5KBr165yz62uro5jx44hKioKR48exbJlyzB58mScO3dOHHv2beTFU7QefPPNN6hduza2bNmCn376Cbt370ZISIi4/W3vDy0tLVy8eBFhYWE4evQopk2bhqCgIMTExJT5h4Wy1NX3+TwoibxzFS973/ryIccovOayxFfo1atXaNOmDdq0aYM///wTlStXRkpKCjw9PWXuTfG/DQBKjKegoACNGzeWO35z0R8J4+Pj8erVK6ipqeHBgweoVq2a3ON9jLpc1usoy+deWRU9lq6urtR6SceV97zkPb/S/tavWbNGKhkLQOrHGED+fSheVtLznT17NqZPny5VNnlqIKZMC5K7PxERERERqSaltzQtV64crKysYG5uLvMlWB47OzukpKRIjZd47do1PH/+HLa2tmJZSkqK2LoLeDPBkpqaGqytreUeU1tbGykpKbCyspJa5I0R+CEKxzX9+++/ceXKFTRv3hz29vbIzc3FqlWr4OjoCAMDA7mvLZyJvmhLxvcVEREBPz8/dO7cGfb29qhSpYo4CUlJGjRogBMnTrzTeRwdHXH16lWZ+2plZQUtLS3Y2toiLy8PFy5cEF9z48aNUift+pD78C7P+uzZs+K/nz59ips3b6Ju3bricc6cOSO1f1RUFKytrWW+fH+s2IuqW7cuUlJSpCZAe9uENnXq1IGurm6Jz9DOzg5xcXFSkxBFRkaW+L55V0XvZ15eHmJjY8X7GRERARcXFwwbNgwODg6wsrKSaflVFhoaGujbty9CQkIQEhKCHj16QE9Pr8T9JRIJmjZtiunTp+PSpUvQ0tLC7t273/3iStGrVy9s2rQJ+/btg5qaGjp06CBue9v7o/CaWrVqhXnz5iEhIUEc/xd4U5/eVpfq1q0rjqVbqOj7DSjb50FZzmVrayv3fVH0s1kR5L3PCu9p0fhyc3Nx4cKFEuO7fv06Hj16hDlz5qB58+aoW7fuO7eIlcfR0RG3bt2CiYmJzHM3NDQEADx58gR+fn6YPHky+vXrB19fX2RlZZV4zNLqclmeXVnY2Ni8tS6VpLTPU3ns7OyQl5cn9cPJ48ePcfPmTannVdLfD3nHNjU1RfXq1fHPP//I3Pd3TS6XZtKkSXj+/LnUEjBx0kc7PhERERERKYbSW5q+q1atWqFBgwbw9fXF4sWLkZeXh2HDhsHNzU2qi56Ojg769u2L+fPn48WLF/D394e3t7dUd+ZCBgYGGD9+PMaMGYOCggI0a9YML168QFRUFPT19dG3b9+Peg3u7u4YM2YMHBwcUL58eQCAq6srNm3ahLFjx5b4OhMTE+jq6uLw4cOoUaMGdHR0xC/Y78rKygq7du1Cx44dIZFIMHXq1Le29AoMDETLli1Ru3Zt9OjRA3l5eTh06BAmTJhQ4msmTpyI7777DsOHD8egQYNQrlw5JCYmihNm2djYoG3bthg0aBB+//13aGhoYPTo0aW2ivyQ+/Auzzo4OBjGxsYwNTXF5MmTUalSJXG26nHjxqFJkyaYMWMGfHx8EB0djd9++01qFuaPHXtRrVu3Ru3atdG3b1/MmzcPL1++FCeCKqnVnI6ODiZOnIgJEyZAS0sLTZs2xcOHD3H16lUMGDAAvr6+CAwMRN++fREUFISHDx9i5MiR6N27t9g99UMsX74cderUga2tLRYtWoSnT5+KXeetrKywYcMGHDlyBLVq1cLGjRsRExPzXomMgQMHikmVyMjIEvc7d+4cTpw4gTZt2sDExATnzp3Dw4cPP3qCz9fXF9OnT8esWbPQrVs36OjoiNve9v7Yv38//vnnH7i6usLIyAgHDx5EQUEBbGxsALyZVf7cuXO4c+eO2MW7uJEjR8LV1RULFy5Ex44dcfLkSRw6dEiqnpTl88DCwgKnT59Gjx49oK2tjUqVKsmcKyAgAN7e3nB0dETLli2xb98+7Nq1C8ePH/9Yt7NMzM3NIZFIsH//frRv3x66urrQ19fHTz/9hICAAFSsWBE1a9bEvHnzkJmZiQEDBsg9TmFr32XLlmHo0KG4cuUKZsyY8cHx+fr64tdff4WXlxeCg4NRo0YNpKSkYNeuXQgICECNGjUwdOhQmJmZYcqUKXj9+jUcHR0xfvx4LF++XOZ4b6vLFhYWOHLkCG7cuAFjY+P3/rsxcuRIDBo0CE5OTnBxccHWrVuRkJAgNURNSUr7PJWnTp068PLywqBBg7B69WoYGBjg559/RvXq1eHl5SXup6mpiZEjR2Lp0qXQ1NTEiBEj8N1335U4bEBQUBD8/f1Rvnx5tGvXDjk5Obhw4QKePn1a6t/fd6GtrS3TcyE776McmoiIiIi+Qp+wYy69hdJbmr4riUSCPXv2wMjICK6urmjVqhUsLS2xdetWqf2srKzQpUsXtG/fHm3atEH9+vVLTWjNmDED06ZNw+zZs2FrawtPT0/s27fvo7Y+KeTh4YH8/HxxbDwAcHNzQ35+fqnjmWpoaGDp0qVYvXo1qlWrJvXF8V0tWrQIRkZGcHFxQceOHeHp6QlHR8dSX+Pu7o7t27dj7969aNSoEVq0aCHTfbq4Bg0aIDw8HLdu3ULz5s3h4OCAqVOnimPlAUBISAjMzMzg5uaGLl26YPDgwTAxMSnxmB96H8r6rOfMmYNRo0ahcePGSEtLw969e8UWbI6Ojti2bRu2bNmC+vXrY9q0aQgODpY7q/bHjL2Quro69uzZg4yMDDRp0gQDBw7ElClTAEAqKVfc1KlTMW7cOEybNg22trbw8fERW87p6enhyJEjePLkCZo0aYJu3bqhZcuW+O23394rxuLmzJmDuXPnomHDhoiIiMDff/8tJt6GDh2KLl26wMfHB99++y0eP36MYcOGvdd56tSpAxcXF9jY2Mh0wS2qfPnyOH36NNq3bw9ra2tMmTIFCxYsQLt27d7rvKXF06RJEyQkJMDX11dq29veHxUqVMCuXbvQokUL2NraYtWqVdi8eTPq1asHABg/fjzU1dVhZ2cndh0vrmnTpli1ahUWLlyIhg0b4vDhwxgzZoxUPSnL50FwcDDu3LmD2rVrS3UhL6pTp05YsmQJfv31V9SrVw+rV69GSEiI1GedIlSvXh3Tp0/Hzz//DFNTU4wYMQLAmzrYtWtX9O7dG46Ojrh9+zaOHDkijjFbXOXKlbF+/Xps374ddnZ2mDNnDubPn//B8enp6eH06dOoWbMmunTpAltbW/Tv3x9ZWVkoX748NmzYgIMHD2Ljxo3Q0NCAnp4eNm3ahLVr1+LgwYMyx3tbXR40aBBsbGzEMYNL+zGhNL6+vpg0aRLGjx8PR0dHJCcnw8/Pr9TPnEKlfZ6WJCQkBI0bN8b3338PZ2dnCIKAgwcPSvVK0dPTw8SJE9GrVy84OztDV1cXW7ZsKfGYAwcOxNq1a7F+/XrY29vDzc0N69ev/yR/64mIiIiI6PMmET5kQDKiL1BYWBg8PDzw9OnTjzoh1acWGRmJZs2a4fbt21KTEynbnTt3UKtWLVy6dAmNGjX65OcTBAF169bFkCFDPlrLsS/NoEGDcP36dURERCg7FPrMtW7dGlWqVMHGjRvlbv9cP08/NrY0Jfp65eS+25jdn1rma9X7QDIqV/qPaERlkfX6w4ci+phWRt9RdghSxrqpzvdDVaWjwv2wk9JLHqbrU6tt8va5bQrNnj0bu3btwvXr16GrqwsXFxfMnTtX7CkJAH5+fggNDZV63bfffis1nFdOTg7Gjx+PzZs3IysrCy1btsSKFSukJnt/+vQp/P39sXfvXgDADz/8gGXLln307xwqXC2IqDS7d++Gvr4+6tSpg9u3b2PUqFFo2rSpSiVMFS09PR0bN27EvXv30K9fP2WHozLmz5+P1q1bo1y5cjh06BBCQ0PfOpQEUXGZmZlYtWoVPD09oa6ujs2bN+P48eM4duyYskMjIiIiIiIlCw8Px/Dhw9GkSRPk5eVh8uTJaNOmDa5duyY1SXbbtm2lJkgu3gNt9OjR2LdvH7Zs2QJjY2OMGzcO33//PWJjY8U5ZHr16oV///0Xhw8fBgAMHjwYvXv3xr59+z7qNTFpSvSZevnyJSZMmIDU1FRUqlQJrVq1woIFC5QdllKZmpqiUqVK+P3330vscv01On/+vDj2raWlJZYuXYqBAwcqOyz6zEgkEhw8eBAzZ85ETk4ObGxssHPnTrRq1UrZoRERERERfbk+kzFNCxOYhUJCQmBiYoLY2Fi4urqK5dra2nLnGwKA58+f448//sDGjRvF7xl//vknzMzMcPz4cXh6eiIxMRGHDx/G2bNnxSH51qxZA2dnZ9y4cUOqZeuHYtKUqBh3d3d8DqNW9OnTB3369FF2GG9lYWGhsPv5OTw3Zdi2bZuyQ6AvgK6u7jtP6vW5fJ4SEREREZGsnJwc5OTkSJXJm/hUnufPnwOAzITFYWFhMDExQYUKFeDm5oZZs2aJ89rExsYiNzcXbdq0EfevVq0a6tevj6ioKHh6eiI6OhqGhoZSc5h89913MDQ0RFRU1EdNmn52E0ERERERERERERHRpzV79mwYGhpKLbNnz37r6wRBwNixY9GsWTPUr19fLG/Xrh02bdqEkydPYsGCBYiJiUGLFi3ExOyDBw+gpaUl03PU1NQUDx48EPeRN3m4iYmJuM/HwpamREREREREREREKkiixP75kyZNkplguSytTEeMGIGEhAScOXNGqtzHx0f8d/369eHk5ARzc3McOHAAXbp0KfF4giBAIvm/+1D03yXt8zEwaUpERERERERERERSytoVv6iRI0di7969OH36tNSM9/JUrVoV5ubmuHXrFgCgSpUqeP36NZ4+fSrV2jQ9PR0uLi7iPv/995/MsR4+fAhTU9N3ivVt2D2fiIiIiIiIiIhIBUkkylvehSAIGDFiBHbt2oWTJ0+iVq1ab33N48ePkZqaiqpVqwIAGjduDE1NTRw7dkzcJy0tDVeuXBGTps7Oznj+/DnOnz8v7nPu3Dk8f/5c3OdjYUtTIiIiIiIiIiIiem/Dhw/HX3/9hb///hsGBgbi+KKGhobQ1dVFRkYGgoKC0LVrV1StWhV37tzB//73P1SqVAmdO3cW9x0wYADGjRsHY2NjVKxYEePHj4e9vT1atWoFALC1tUXbtm0xaNAgrF69GgAwePBgfP/99x91EiiASVMiIiIiIiIiIiL6ACtXrgQAuLu7S5WHhITAz88P6urquHz5MjZs2IBnz56hatWq8PDwwNatW2FgYCDuv2jRImhoaMDb2xtZWVlo2bIl1q9fD3V1dXGfTZs2wd/fH23atAEA/PDDD/jtt98++jVJBEEQPvpRiYiIiAgAkJ2n7AiISFlycguUHYKUzNeq94FkVE5L2SHQFyDrdb6yQ5CyMvqOskOQMtattrJDUHk6Ktyk8M6jbKWd26KSjtLOrQo4pikRERERERERERFRESqcSyciIiIiIiIiIvqKveOETPTxsKUpERERERERERERUREc05SIiIjoE+KYpkRERKRMr/NUa3xlANDSUK02fKo8pundxzlKO7e5sbbSzq0KVKuWEhERERERERERESkZk6ZERERERERERERERahwA2QiIiIiIiIiIqKvl4QTQSkNW5oSERERERERERERFcGWpkRERERERERERCqIDU2Vhy1NiYiIiIiIiIiIiIpg0pSIiIiIiIiIiIioCHbPJyIiIiIiIiIiUkGcCEp52NKUiIiIiIiIiIiIqAi2NCUiIiIiIiIiIlJJbGqqLGxpSkRERERERERERFQEk6ZEJFdQUBAaNWpU6j5+fn7o1KmTQuL5EBKJBHv27Cl1n8/lWj6Eu7s7Ro8erewwyiwsLAwSiQTPnj1TdihERERERERKIZEob/naMWlK9Jnz8/ODRCKBRCKBpqYmLC0tMX78eLx69UrZoamMtLQ0tGvXDgBw584dSCQSxMXFKTcooi/Eh7ynyvKDhiphIp+IiIiI6OvBMU2JvgBt27ZFSEgIcnNzERERgYEDB+LVq1dYuXKlzL65ubnQ1NRUQpTKU6VKFWWH8Mm9fv0aWlpayg6DPnP5+fmQSCRQU+NvqkRERERE9HXjtyKiL4C2tjaqVKkCMzMz9OrVC76+vmLrrcJu9uvWrYOlpSW0tbUhCAJSUlLg5eUFfX19lC9fHt7e3vjvv/9kjr169WqYmZlBT08P3bt3L7WFlSAImDdvHiwtLaGrq4uGDRtix44d4vbCVlpHjhyBg4MDdHV10aJFC6Snp+PQoUOwtbVF+fLl0bNnT2RmZpZ4jsqVK2Pnzp1iWaNGjWBiYiKuR0dHQ1NTExkZGQCkW7PVqlULAODg4ACJRAJ3d3ep48+fPx9Vq1aFsbExhg8fjtzc3BKvNykpCV5eXjA1NYW+vj6aNGmC48ePl7g/8H/Po7T7Kq8bfadOneDn5yeuW1hYYObMmfDz84OhoSEGDRoEAIiMjISbmxv09PRgZGQET09PPH36VHxdQUEBJkyYgIoVK6JKlSoICgqSOs/ChQthb2+PcuXKwczMDMOGDRPvIwDcvXsXHTt2hJGREcqVK4d69erh4MGD4vZr166hffv20NfXh6mpKXr37o1Hjx6VeD/edjwAiI2NhZOTE/T09ODi4oIbN25IbV+5ciVq164NLS0t2NjYYOPGjeK2cePGoWPHjuL64sWLIZFIcODAAbHMxsYGq1evBvB/wzS8Sz14WwzAmzq4du1adO7cGXp6eqhTpw727t1b4vGCg4Nhb28vU964cWNMmzYNwJtnGRwcjBo1akBbWxuNGjXC4cOHxX3ltYqMi4uDRCLBnTt3AADr169HhQoVsH//ftjZ2UFbWxt3796VOe/Tp0/h6+uLypUrQ1dXF3Xq1EFISAiAkt9TMTExaN26NSpVqgRDQ0O4ubnh4sWL4jEtLCwAAJ07d4ZEIhHXAWDfvn1o3LgxdHR0YGlpienTpyMvL0/qfq5evRrff/899PT0YGtri+joaNy+fRvu7u4oV64cnJ2dkZSUJHUdZTluSc/pzp078PDwAAAYGRlBIpFIvSeJiIiIiD4FiRKXrx2TpkRfIF1dXakkz+3bt7Ft2zbs3LlT7ELbqVMnPHnyBOHh4Th27BiSkpLg4+MjdZzC1+3btw+HDx9GXFwchg8fXuJ5p0yZgpCQEKxcuRJXr17FmDFj8OOPPyI8PFxqv6CgIPz222+IiopCamoqvL29sXjxYvz11184cOAAjh07hmXLlsk9h0QigaurK8LCwgC8SeZcu3YNubm5uHbtGoA3yaLGjRtDX19f5vXnz58HABw/fhxpaWnYtWuXuO3UqVNISkrCqVOnEBoaivXr12P9+vUlXm9GRgbat2+P48eP49KlS/D09ETHjh2RkpJS4muAd7+vJfn1119Rv359xMbGYurUqYiLi0PLli1Rr149REdH48yZM+jYsSPy8/PF14SGhqJcuXI4d+4c5s2bh+DgYBw7dkzcrqamhqVLl+LKlSsIDQ3FyZMnMWHCBHH78OHDkZOTg9OnT+Py5cuYO3eueJ/T0tLg5uaGRo0a4cKFCzh8+DD+++8/eHt7l3gNpR2v0OTJk7FgwQJcuHABGhoa6N+/v7ht9+7dGDVqFMaNG4crV65gyJAh6NevH06dOgXgTQI6IiICBQUFAIDw8HBUqlRJrJMPHjzAzZs34ebmJh7zXevB22IoNH36dHh7eyMhIQHt27eHr68vnjx5IveY/fv3x7Vr1xATEyOWJSQk4NKlS2KibsmSJViwYAHmz5+PhIQEeHp64ocffsCtW7dKjFWezMxMzJ49G2vXrsXVq1elfoAoNHXqVFy7dg2HDh1CYmIiVq5ciUqVKgEo+T318uVL9O3bFxERETh79izq1KmD9u3b4+XLlwAgXltISAjS0tLE9SNHjuDHH3+Ev78/rl27htWrV2P9+vWYNWuWVEwzZsxAnz59EBcXh7p166JXr14YMmQIJk2ahAsXLgAARowYIe5f1uOW9JzMzMzEH2tu3LiBtLQ0LFmy5J3uNRERERERfUYEIvqs9e3bV/Dy8hLXz507JxgbGwve3t6CIAhCYGCgoKmpKaSnp4v7HD16VFBXVxdSUlLEsqtXrwoAhPPnz4uvU1dXF1JTU8V9Dh06JKipqQlpaWky587IyBB0dHSEqKgoqfgGDBgg9OzZUxAEQTh16pQAQDh+/Li4ffbs2QIAISkpSSwbMmSI4OnpWeI1L126VKhfv74gCIKwZ88ewcnJSejSpYuwfPlyQRAEoU2bNsLEiRPF/QEIu3fvFgRBEJKTkwUAwqVLl2Tuo7m5uZCXlyeWde/eXfDx8SkxDnns7OyEZcuWlbi9LPfVzc1NGDVqlNTrvLy8hL59+4rr5ubmQqdOnaT26dmzp9C0adMSz+3m5iY0a9ZMqqxJkyZS96q4bdu2CcbGxuK6vb29EBQUJHffqVOnCm3atJEqS01NFQAIN27ckPua0o4nr74cOHBAACBkZWUJgiAILi4uwqBBg6Re1717d6F9+/aCIAjCs2fPBDU1NeHChQtCQUGBYGxsLMyePVto0qSJIAiC8Ndffwmmpqbia9+nHrwtBkF4UwenTJkirmdkZAgSiUQ4dOhQicdt166d8NNPP4nro0ePFtzd3cX1atWqCbNmzZJ6TZMmTYRhw4YJgvB/9+/p06fi9kuXLgkAhOTkZEEQBCEkJEQAIMTFxZUYhyAIQseOHYV+/frJ3VbSe6q4vLw8wcDAQNi3b59YVvS9Wah58+bCL7/8IlW2ceNGoWrVqlKvK3o/o6OjBQDCH3/8IZZt3rxZ0NHR+aDjFn9O8u5pcdnZ2cLz58+llmcZ2UJWrsCFCxcuXLhw4aKU5XlWvsotyr4nxRdVdv9ZjtKWrx1bmhJ9Afbv3w99fX3o6OjA2dkZrq6uUi01zc3NUblyZXE9MTERZmZmMDMzE8vs7OxQoUIFJCYmimU1a9ZEjRo1xHVnZ2cUFBTIdI8G3nTLzs7ORuvWraGvry8uGzZskOki26BBA/Hfpqam0NPTg6WlpVRZenp6idfr7u6Oq1ev4tGjRwgPD4e7uzvc3d0RHh6OvLw8REVFSbUcLKt69epBXV1dXK9atWqpcbx69QoTJkwQ752+vj6uX7/+1pam73JfS+Pk5CS1XtjStDRF7z0ge42nTp1C69atUb16dRgYGKBPnz54/PixOLGYv78/Zs6ciaZNmyIwMBAJCQnia2NjY3Hq1Cmp51+3bl0AkKkDhUo7nryYq1atCgBizImJiWjatKnU/k2bNhXrsaGhIRo1aoSwsDBcvnwZampqGDJkCOLj4/Hy5UuEhYXJ1JV3rQdvi0HedZQrVw4GBgalHnfQoEHYvHkzsrOzkZubi02bNomtbF+8eIH79++X6bxvo6WlJVMvivvpp5+wZcsWNGrUCBMmTEBUVNRbj5ueno6hQ4fC2toahoaGMDQ0REZGxlvfH7GxsQgODpaqR4MGDUJaWprUsB3FP0cASA1pYGpqiuzsbLx48eK9j1uW51Tc7NmzxestXH6dO7vMryciIiIiItXAiaCIvgAeHh5YuXIlNDU1Ua1aNZmJnsqVKye1LggCJBLZEUpKKi9UuE3ePoXdnw8cOIDq1atLbdPW1pZaLxqfRCKRiVcikYjHk6d+/fowNjZGeHg4wsPDERwcDDMzM8yaNQsxMTHIyspCs2bNSnx9Sd41joCAABw5cgTz58+HlZUVdHV10a1bN7x+/fqdzlv8vqqpqUEQBKl95I2pWfy56urqvvVcpV3j3bt30b59ewwdOhQzZsxAxYoVcebMGQwYMEA8/8CBA+Hp6YkDBw7g6NGjmD17NhYsWICRI0eioKAAHTt2xNy5c2XOW5jsLK6048mLufAeFX0uxetj8Xrs7u6OsLAwaGlpwc3NDUZGRqhXrx4iIyMRFhYmM37su9aDssTwPsft2LEjtLW1sXv3bmhrayMnJwddu3Yt83kLJ3MqWpfk1SNdXd1S3/cA0K5dO9y9excHDhzA8ePH0bJlSwwfPhzz588v8TV+fn54+PAhFi9eDHNzc2hra8PZ2fmt74+CggJMnz4dXbp0kdmmo6Mj/ltevSitrrzPcQuP87bnX9SkSZMwduxYqTJBXbuEvYmIiIiISFWxpSnRF6BcuXKwsrKCubm5zBd+eezs7JCSkoLU1FSx7Nq1a3j+/DlsbW3FspSUFNy/f19cj46OhpqaGqytreUeU1tbGykpKbCyspJairZo/RgKxzX9+++/ceXKFTRv3hz29vbIzc3FqlWr4OjoCAMDA7mvLZxhvug4n+8rIiICfn5+6Ny5M+zt7VGlShVxgp3SvO2+Vq5cGWlpaeL2/Px8XLly5a3HbdCgAU6cOPHuF/L/XbhwAXl5eViwYAG+++47WFtbS8VZyMzMDEOHDsWuXbswbtw4rFmzBgDg6OiIq1evwsLCQqYOFE/wluV4ZWFra4szZ85IlUVFRUnV48JxTU+ePClOUuTm5oYtW7bIjGf6PsoSw/vQ0NBA3759ERISgpCQEPTo0QN6enoAgPLly6NatWqlnrewdXnRulQ4pvH7qFy5Mvz8/PDnn39i8eLF+P333wGU/J6KiIiAv78/2rdvj3r16kFbW1tmUjBNTU2Z1zk6OuLGjRsydcjKykpMBL+Pj3Hcsnx+aGtro3z58lJL8R+OiIiIiIjKSqLE/752bGlK9BVq1aoVGjRoAF9fXyxevBh5eXkYNmwY3NzcpLp86+jooG/fvpg/fz5evHgBf39/eHt7o0qVKjLHNDAwwPjx4zFmzBgUFBSgWbNmePHiBaKioqCvr4++fft+1Gtwd3fHmDFj4ODggPLlywMAXF1dsWnTJplWXkWZmJhAV1cXhw8fRo0aNaCjowNDQ8P3isHKygq7du1Cx44dIZFIMHXq1DK1SHvbfW3RogXGjh2LAwcOoHbt2li0aJHUDOglmTRpEuzt7TFs2DAMHToUWlpaOHXqFLp37y5O2lOa2rVrIy8vD8uWLUPHjh0RGRmJVatWSe0zevRotGvXDtbW1nj69ClOnjwpJumGDx+ONWvWoGfPnggICEClSpVw+/ZtbNmyBWvWrJHq8l6W45VFQEAAvL294ejoiJYtW2Lfvn3YtWsXjh8/Lu7j6uqKly9fYt++fZg5cyaAN/Wna9euqFy5Muzs7Mp8vveN4X0NHDhQvB+RkZEy5w0MDETt2rXRqFEjhISEIC4uDps2bQIA8QeLoKAgzJw5E7du3cKCBQveK45p06ahcePGqFevHnJycrB//34xrpLeU1ZWVti4cSOcnJzw4sULBAQEyLSGtrCwwIkTJ9C0aVNoa2vDyMgI06ZNw/fffw8zMzN0794dampqSEhIwOXLl8Xn977X8KHHNTc3h0Qiwf79+9G+fXvo6urKnXCOiIiIiIg+f2xpSvQVkkgk2LNnD4yMjODq6opWrVrB0tISW7duldrPysoKXbp0Qfv27dGmTRvUr18fK1asKPG4M2bMwLRp0zB79mzY2trC09MT+/btQ61atT76NXh4eCA/P19sOQi8aT2Yn59fastBDQ0NLF26FKtXr0a1atXg5eX13jEsWrQIRkZGcHFxQceOHeHp6QlHR8e3vu5t97V///7o27cv+vTpAzc3N9SqVQseHh5vPa61tTWOHj2K+Ph4fPPNN3B2dsbff/8NDY2y/T7WqFEjLFy4EHPnzkX9+vWxadMmzJ4tPRZjfn4+hg8fDltbW7Rt2xY2NjZi7NWqVUNkZCTy8/Ph6emJ+vXrY9SoUTA0NCyxJV9pxyuLTp06YcmSJfj1119Rr149rF69GiEhIVL1wtDQEA4ODqhYsaKYIG3evDkKCgo+uJVpWWN4X3Xq1IGLiwtsbGzw7bffSm3z9/fHuHHjMG7cONjb2+Pw4cPYu3cv6tSpA+BNK87Nmzfj+vXraNiwIebOnfveSUctLS1MmjQJDRo0gKurK9TV1bFlyxYAJb+n1q1bh6dPn8LBwQG9e/eGv78/TExMpI67YMECHDt2DGZmZnBwcAAAeHp6Yv/+/Th27BiaNGmC7777DgsXLoS5ufl7xV7oYxy3evXqmD59On7++WeYmppixIgRHxQTEREREdFbSZS4fOUkQvGB84iI6JMJCgrCnj17PqibNH09BEFA3bp1MWTIkFJbUJNqy85TdgRERET0NXudV/bx2RVFS0O12vDpqHA/7AcvZOclUJQq5d8+/N+XTIWrBRER0dcrPT0dGzduxL1799CvXz9lh0NERERERErABp/Kw6QpERGRCjI1NUWlSpXw+++/w8jISNnhEBERERERfVXYPZ+IiIjoE2L3fCIiIlImds9/O1Xunv+fErvnm7J7PhEREREREREREakaCfvnK41qpfaJiIiIiIiIiIiIlIwtTYmIiIiIiIiIiFSQhFNBKQ1bmhIREREREREREREVwaQpERERERERERERURHsnk9ERERERERERKSK2DtfadjSlIiIiIiIiIiIiKgItjQlIiIiIiIiIiJSQWxoqjxsaUpERERERERERERUBFuaEhEREX1lBEHZEUiTsAkFERHRJ6OloXrt5QoKVOx/Rtiek+Rg0pSIiIiIiIiIiEgF8cdl5VG9nxuIiIiIiIiIiIiIlIgtTYmIiIiIiIiIiFSQhEMHKA1bmhIREREREREREREVwZamREREREREREREKohjmioPW5oSERERERERERERFcGkKREREREREREREVERTJoSERERERERERERFcGkKREREREREREREVERnAiKiIiIiIiIiIhIBXEiKOVhS1MiIiIiIiIiIiKiIpg0JSKid2ZhYYHFixcrOwyF8PPzQ6dOncR1QRAwePBgVKxYERKJBHFxcXB3d8fo0aM/aRx37twRz6dsEokEe/bsUXYYpXrXGIs/ZyIiIiIi+roxaUpE9BVYtWoVDAwMkJeXJ5ZlZGRAU1MTzZs3l9o3IiICEokEN2/eLPF4MTExGDx4sLguL0EVFBSERo0afZT4FaGkpOSSJUuwfv16cf3w4cNYv3499u/fj7S0NNSvXx+7du3CjBkzPlos8hJ4ZmZm4vm+FqqWnFdEcpyIiIiIqCiJEv/72jFpSkT0FfDw8EBGRgYuXLgglkVERKBKlSqIiYlBZmamWB4WFoZq1arB2tpa5jivX78GAFSuXBl6enqfPnAVYGhoiAoVKojrSUlJqFq1KlxcXFClShVoaGigYsWKMDAw+KRxqKuri+cjIiIiIiKiT4tJUyKir4CNjQ2qVauGsLAwsSwsLAxeXl6oXbs2oqKipMo9PDwA/F+Lx9mzZ0slUou2ALSwsAAAdO7cGRKJBBYWFli/fj2mT5+O+Ph4SCQSSCQSsbXm8+fPMXjwYJiYmKB8+fJo0aIF4uPjxfMXtlDduHEjLCwsYGhoiB49euDly5clXt/69etRoUIFHDlyBLa2ttDX10fbtm2RlpYm7lNQUIDg4GDUqFED2traaNSoEQ4fPixur1WrFgDAwcEBEokE7u7uUveg8N8jR45ESkqKeK2AbAvEnJwcTJgwAWZmZtDW1kadOnXwxx9/AADy8/MxYMAA1KpVC7q6urCxscGSJUukrj80NBR///23eO/CwsLktoQNDw/HN998A21tbVStWhU///yzVGtid3d3+Pv7Y8KECahYsSKqVKmCoKCgEu8j8KYVcevWrVGpUiUYGhrCzc0NFy9elNkvLS0N7dq1g66uLmrVqoXt27dLbb98+TJatGgBXV1dGBsbY/DgwcjIyJCKrXirzU6dOsHPz0/cfvfuXYwZM0a8DyW5desWXF1doaOjAzs7Oxw7dkxmn3v37sHHxwdGRkYwNjaGl5cX7ty5I7Pf9OnTxbo5ZMgQ8YcCPz8/hIeHY8mSJWI88l5PRERERPQxSSTKW752TJoSEX0l3N3dcerUKXH91KlTcHd3h5ubm1j++vVrREdHi0lTADhx4gQSExNx7Ngx7N+/X+a4MTExAICQkBCkpaUhJiYGPj4+GDduHOrVq4e0tDSkpaXBx8cHgiCgQ4cOePDgAQ4ePIjY2Fg4OjqiZcuWePLkiXjMpKQk7NmzB/v378f+/fsRHh6OOXPmlHp9mZmZmD9/PjZu3IjTp08jJSUF48ePF7cvWbIECxYswPz585GQkABPT0/88MMPuHXrFgDg/PnzAIDjx48jLS0Nu3btkjnHkiVLxMRr4bXK06dPH2zZsgVLly5FYmIiVq1aBX19fQBvkrc1atTAtm3bcO3aNUybNg3/+9//sG3bNgDA+PHj4e3tLSZ909LS4OLiInOOe/fuoX379mjSpAni4+OxcuVK/PHHH5g5c6bUfqGhoShXrhzOnTuHefPmITg4WG5SsdDLly/Rt29fRERE4OzZs6hTpw7at28vk7SeOnUqunbtivj4ePz444/o2bMnEhMTxWfRtm1bGBkZISYmBtu3b8fx48cxYsSIEs9b3K5du1CjRg0EBweL90GegoICdOnSBerq6jh79ixWrVqFiRMnSu2TmZkJDw8P6Ovr4/Tp0zhz5oyYWC9MigL/V9dPnTqFzZs3Y/fu3Zg+fTqAN8/e2dkZgwYNEuMxMzMr8/UQEREREdHnhX38iIi+Eu7u7hgzZgzy8vKQlZWFS5cuwdXVFfn5+Vi6dCkA4OzZs8jKypJKmpYrVw5r166FlpaW3ONWrlwZAFChQgVUqVJFLNfX14eGhoZU2cmTJ3H58mWkp6dDW1sbADB//nzs2bMHO3bsEMdJLSgowPr168Uu771798aJEycwa9asEq8vNzcXq1atQu3atQEAI0aMQHBwsLh9/vz5mDhxInr06AEAmDt3Lk6dOoXFixdj+fLl4nUYGxtLxVyUoaEhDAwMxK7y8ty8eRPbtm3DsWPH0KpVKwCApaWluF1TU1NMxAFvWrhGRUVh27Zt8Pb2hr6+PnR1dZGTk1PiOQBgxYoVMDMzw2+//QaJRIK6devi/v37mDhxIqZNmwY1tTe/izZo0ACBgYEAgDp16uC3337DiRMn0Lp1a7nHbdGihdT66tWrYWRkhPDwcHz//fdieffu3TFw4EAAwIwZM3Ds2DEsW7YMK1aswKZNm5CVlYUNGzagXLlyAIDffvsNHTt2xNy5c2FqalridRWqWLEi1NXVYWBgUOp9OH78OBITE3Hnzh3UqFEDAPDLL7+gXbt24j5btmyBmpoa1q5dK7ZYDQkJQYUKFRAWFoY2bdoAALS0tLBu3Tro6emhXr16CA4ORkBAAGbMmAFDQ0NoaWlBT0+v1HhycnKQk5MjVSaoa4v1nYiIiIjoXbDBp/KwpSkR0VfCw8MDr169QkxMDCIiImBtbQ0TExO4ubkhJiYGr169QlhYGGrWrCmV5LO3ty8xYfquYmNjkZGRAWNjY+jr64tLcnIykpKSxP0sLCykxgitWrUq0tPTSz22np6emDAt/poXL17g/v37aNq0qdRrmjZtKraO/Fji4uKgrq4ONze3EvdZtWoVnJycULlyZejr62PNmjVISUl5p/MkJibC2dlZqtt606ZNkZGRgX///Vcsa9CggdTr3nYv09PTMXToUFhbW8PQ0BCGhobIyMiQic/Z2VlmvfBeJiYmomHDhmLCtDC2goIC3Lhx452u820SExNRs2ZNMWEqL7bY2Fjcvn0bBgYGYp2rWLEisrOzpepdw4YNpcbqdXZ2RkZGBlJTU8scz+zZs8X7Vrj8Onf2B1whEREREREpA1uaEhF9JaysrFCjRg2cOnUKT58+FZN6VapUQa1atRAZGYlTp07JtDQsmvj6UAUFBahatarU2KqFik62pKmpKbVNIpGgoKCg1GPLe40gCDJlRQmCUOpYme9DV1e31O3btm3DmDFjsGDBAjg7O8PAwAC//vorzp07907nkRd74fUWLX/Xe+nn54eHDx9i8eLFMDc3h7a2NpydnaW6sZek8Lyl3dfCcjU1NZnnk5ub+9ZzFFf8GEXPUaigoACNGzfGpk2bZPYtbGFcmnepI5MmTcLYsWOlY1RnK1MiIiIios8NW5oSEX1FPDw8EBYWhrCwMHGiIwBwc3PDkSNHcPbsWamu+WWlqamJ/Px8qTItLS2ZMkdHRzx48AAaGhqwsrKSWipVqvRe11QW5cuXR7Vq1XDmzBmp8qioKNja2orxApCJ+V3Z29ujoKAA4eHhcrdHRETAxcUFw4YNg4ODA6ysrKRaOxbG8rY47OzsEBUVJZU0jIqKgoGBAapXr/7e8UdERMDf3x/t27dHvXr1oK2tjUePHsnsd/bsWZn1unXrirHFxcXh1atX4vbIyEioqamJk4lVrlxZapzS/Px8XLlyReqYZb0PKSkpuH//vlgWHR0ttY+joyNu3boFExMTmXpnaGgo7hcfH4+srCypa9LX1xdbsZYlHm1tbZQvX15qYdd8IiIiInpvEiUuXzkmTYmIviIeHh44c+YM4uLipLqPu7m5Yc2aNcjOzn6vpKmFhQVOnDiBBw8e4OnTp2JZcnIy4uLi8OjRI+Tk5KBVq1ZwdnZGp06dcOTIEdy5cwdRUVGYMmUKLly48NGuU56AgADMnTsXW7duxY0bN/Dzzz8jLi4Oo0aNAgCYmJhAV1cXhw8fxn///Yfnz5+/13ksLCzQt29f9O/fH3v27EFycjLCwsLEiZ6srKxw4cIFHDlyBDdv3sTUqVNlJpSysLBAQkICbty4gUePHsltgTls2DCkpqZi5MiRuH79Ov7++28EBgZi7Nix4nim78PKygobN25EYmIizp07B19fX7mtZ7dv345169bh5s2bCAwMxPnz58WJnnx9faGjo4O+ffviypUrOHXqFEaOHInevXuL45m2aNECBw4cwIEDB3D9+nUMGzYMz549k7kPp0+fxr179+QmbgGgVatWsLGxQZ8+fRAfH4+IiAhMnjxZah9fX19UqlQJXl5eiIiIQHJyMsLDwzFq1CipoQxev36NAQMG4Nq1azh06BACAwMxYsQI8X5aWFjg3LlzuHPnDh49evTW1s9ERERERPT5YtKUiOgr4uHhgaysLFhZWUlNxuPm5oaXL1+idu3a7zUj+IIFC3Ds2DGYmZnBwcEBANC1a1e0bdsWHh4eqFy5MjZv3gyJRIKDBw/C1dUV/fv3h7W1NXr06IE7d+6UaXKgD+Hv749x48Zh3LhxsLe3x+HDh7F3717UqVMHAKChoYGlS5di9erVqFatGry8vN77XCtXrkS3bt0wbNgw1K1bF4MGDRJbXQ4dOhRdunSBj48Pvv32Wzx+/BjDhg2Tev2gQYNgY2MjjnsaGRkpc47q1avj4MGDOH/+PBo2bIihQ4diwIABmDJlynvHDQDr1q3D06dP4eDggN69e8Pf3x8mJiYy+02fPh1btmxBgwYNEBoaik2bNsHOzg7Am/Fljxw5gidPnqBJkybo1q0bWrZsid9++018ff/+/dG3b1/06dMHbm5uqFWrlkzCPjg4GHfu3EHt2rVL7EavpqaG3bt3IycnB9988w0GDhwoM2GYnp4eTp8+jZo1a6JLly6wtbVF//79kZWVhfLly4v7tWzZEnXq1IGrqyu8vb3RsWNHBAUFidvHjx8PdXV12NnZoXLlyu88Di0RERER0buSKPG/r51EkDcYGBERERF9FNl5yo5Alqr9399HHlqYiIiIVFxBgWr9z4ielur+z0hGjvLulb626t4XRWBLUyIiIiIiIiIiIqIiNJQdABEREREREREREclijxzlYUtTIiIiIiIiIiIioiLY0pSIiIiIiIiIiEgFsaGp8rClKREREREREREREVERbGlKRERERERERESkitjUVGnY0pSIiIiIiIiIiIioCCZNiYiIiIiIiIiIiIpg93wiIiIiIiIiIiIVJGH/fKVhS1MiIiIiIiIiIiL6YCtWrECtWrWgo6ODxo0bIyIiQtkhvTcmTYmIiIiIiIiIiFSQRKK85V1t3boVo0ePxuTJk3Hp0iU0b94c7dq1Q0pKyse/MQogEQRBUHYQRERERF+q7DxlRyBL1f7v733+p5yIiIg+XwUFqvU/I3paqvs/I8r8f0mddxzU89tvv4WjoyNWrlwpltna2qJTp06YPXv2R47u02NLUyIiIiIiIiIiIpKSk5ODFy9eSC05OTly9339+jViY2PRpk0bqfI2bdogKipKEeF+fAIRERERqbzs7GwhMDBQyM7OVnYogiCoXjyCoHoxqVo8gqB6MTGet1O1mFQtHkFQvZhULR5BUL2YVC0eQVC9mFQtHkFQvZhULZ4vUWBgoABAagkMDJS777179wQAQmRkpFT5rFmzBGtrawVE+/Gxez4RERHRZ+DFixcwNDTE8+fPUb58eWWHo3LxAKoXk6rFA6heTIzn7VQtJlWLB1C9mFQtHkD1YlK1eADVi0nV4gFULyZVi+dLlJOTI9OyVFtbG9ra2jL73r9/H9WrV0dUVBScnZ3F8lmzZmHjxo24fv36J4/3Y3vH0QmIiIiIiIiIiIjoS1dSglSeSpUqQV1dHQ8ePJAqT09Ph6mp6acI75PjmKZERERERERERET03rS0tNC4cWMcO3ZMqvzYsWNwcXFRUlQfhi1NiYiIiIiIiIiI6IOMHTsWvXv3hpOTE5ydnfH7778jJSUFQ4cOVXZo74VJUyIiIqLPgLa2NgIDA8vcRepTU7V4ANWLSdXiAVQvJsbzdqoWk6rFA6heTKoWD6B6MalaPIDqxaRq8QCqF5OqxUOAj48PHj9+jODgYKSlpaF+/fo4ePAgzM3NlR3ae+FEUERERERERERERERFcExTIiIiIiIiIiIioiKYNCUiIiIiIiIiIiIqgklTIiIiIiIiIiIioiKYNCUiIiIiok8uNzcXHh4euHnzprJDUWkbNmxATk6OTPnr16+xYcMGhceTkpICedNgCIKAlJQUhcdDZaNq9YjoY+DnESkak6ZEREREKujixYu4fPmyuP7333+jU6dO+N///ofXr18rMTLVYWlpicePH8uUP3v2DJaWlgqPp0WLFnj27JlM+YsXL9CiRQuFxyPPixcvsGfPHiQmJir83Jqamrhy5QokEonCz12S0NBQHDhwQFyfMGECKlSoABcXF9y9e1cpMfXr1w/Pnz+XKX/58iX69eun8Hhq1aqFhw8fypQ/efIEtWrVUng88uTn5yMuLg5Pnz5VyvmDg4ORmZkpU56VlYXg4GAlRKR69QgA8vLycPz4caxevRovX74EANy/fx8ZGRlKiadQeno6rly5goSEBKmFgP79+4vPqqhXr16hf//+Co/nc/g8oi+LRJCXpiciIiIipWrSpAl+/vlndO3aFf/88w/q1auHzp07IyYmBh06dMDixYs/eQxjx44t874LFy78hJHIp6amhgcPHsDExESq/L///kPNmjXltrJSRjzp6emoXr06cnNzFRoPAHh7e8PV1RUjRoxAVlYWGjZsiDt37kAQBGzZsgVdu3ZVaDzjxo2DpqYm5syZo9DzlsTGxgYrV65EixYtEB0djZYtW2Lx4sXYv38/NDQ0sGvXLoXHpKamhv/++w+VK1eWKo+Pj4eHhweePHmiEvHcvXsXdnZ2ePXqlULjAYDRo0fD3t4eAwYMQH5+Ptzc3BAVFQU9PT3s378f7u7uCo1HXV0daWlpMu/9x48fw8TEBPn5+QqNB1C9enT37l20bdsWKSkpyMnJwc2bN2FpaYnRo0cjOzsbq1atUmg8ABAbG4u+ffsiMTFRbL0okUggCAIkEonCn9vjx48xbdo0nDp1Cunp6SgoKJDaruhnBpRctx89eoQqVaogLy9PofGo4ucRfdk0lB0AEREREcm6efMmGjVqBADYvn07XF1d8ddffyEyMhI9evRQSNL00qVLUuuxsbHIz8+HjY2NGKO6ujoaN278yWMpau/eveK/jxw5AkNDQ3E9Pz8fJ06cgIWFhcLiKdoi6dq1a3jw4IFUPIcPH0b16tUVFk9Rp0+fxuTJkwEAu3fvhiAIePbsGUJDQzFz5kyFJ01fv36NtWvX4tixY3ByckK5cuWktis6+Z6amgorKysAwJ49e9CtWzcMHjwYTZs2VXjizcHBARKJBBKJBC1btoSGxv99VcvPz0dycjLatm2rsHgKfzSRSCSYOnUq9PT0pOI5d+6c+BmlaDt27MCPP/4IANi3bx+Sk5Nx/fp1bNiwAZMnT0ZkZKRC4ylMshUXHx+PihUrKjQWVatHhUaNGgUnJyfEx8fD2NhYLO/cuTMGDhyo8HiAN61xra2t8ccff8DU1FTpreB//PFHJCUlYcCAAUqP58WLFxAEAYIg4OXLl9DR0RG35efn4+DBgzKJ1E9JlT+P6MvGpCkRERGRChIEQWxlcvz4cXz//fcAADMzMzx69EghMZw6dUr898KFC2FgYIDQ0FAYGRkBAJ4+fYp+/fqhefPmComnUKdOnQC8+fLUt29fqW2ampqwsLDAggULFBZPo0aNxCSFvG74urq6WLZsmcLiKer58+di0ubw4cPo2rUr9PT00KFDBwQEBCg8nitXrsDR0REAZMY2VUaCQF9fH48fP0bNmjVx9OhRjBkzBgCgo6ODrKwshcZSWK/j4uLg6ekJfX19cZuWlhYsLCwUmuQu/NFEEARcvnwZWlpaUvE0bNgQ48ePV1g8RRW2cgOAgwcPonv37rC2tsaAAQOwdOlShcVhZGQkvvetra2l6nB+fj4yMjIwdOhQhcUDqF49KnTmzBlERkZK1SMAMDc3x7179xQeDwAkJydj165d4g8nynbmzBmcOXMGDRs2VHYoqFChglTdLk4ikWD69OkKi0eVP4/oy8akKREREZEKcnJywsyZM9GqVSuEh4dj5cqVAN58yTM1NVV4PAsWLMDRo0fFhCnwJmEwc+ZMtGnTBuPGjVNYLIXJ5Fq1aiEmJgaVKlVS2LnlSU5OhiAIsLS0xPnz56W6DWppacHExATq6upKic3MzAzR0dGoWLEiDh8+jC1btgB4k/Au2nJIUYom4lVB69atMXDgQDg4OODmzZvo0KEDAODq1asKba0MAIGBgcjPz4e5uTk8PT1RtWpVhZ6/uMJn5efnh2XLlsHAwECp8RRlamqKa9euoWrVqjh8+DBWrFgBAMjMzFToe23x4sUQBAH9+/fH9OnTpVq9FyYonZ2dFRYP8KYeAYCFhQV8fHyU8j6Xp6CgQG5393///Vdpdatly5aIj49XmaRp3bp1Ff5jTUlOnToFQRDQokUL7Ny5U6rFtJaWFszNzVGtWjWFxgO8aR28ZMkSlC9fXmHnpq8bxzQlIiIiUkEJCQnw9fVFSkoKxo4dK34RHjlyJB4/foy//vpLofEYGBjg77//lmlJefLkSXh5ecmdKIKUb8WKFRg1ahT09fVRs2ZNXLp0CWpqali2bBl27dqltCTm7du3kZSUBFdXV+jq6pbYvflTe/bsGaZMmYLU1FT89NNPYrflwMBAaGlpiUMbKJKOjg4SExNVYlKTvLw86OjoIC4uDvXr11d2OKKgoCAsXrwYVatWRWZmJm7evAltbW2sW7cOa9asQXR0tELjCQ8Ph4uLCzQ1NRV63rJ4/fq13PExa9asqdA4fHx8YGhoiN9//x0GBgZISEhA5cqV4eXlhZo1ayIkJESh8QBvWiz37dsX33zzDerXry/z/H744QeFxhMTE4Off/4Z06ZNkxuPMhKFd+/ehZmZGdTUVG8O8dTUVEgkEtSoUUPZodAXjElTIiIios9IdnY21NXVFf7lvE+fPggPD8eCBQvw3XffAQDOnj2LgIAAuLq6IjQ0VKHxFDpx4gROnDghNymwbt06hcdz8+ZNhIWFyY1n2rRpCo8HAC5cuIDU1FS0bt1a7Kp74MABVKhQAU2bNlVoLI8fP4a3tzdOnToFiUSCW7duwdLSEgMGDECFChUUOqyCqmrSpAnmzJmDli1bKjsUAEDt2rWxa9culegyXNSOHTuQmpqK7t27i0mT0NBQVKhQAV5eXgqPp6CgALdv35b73nd1dVV4PLdu3UL//v0RFRUlVa6sSY7u378PDw8PqKur49atW3BycsKtW7dQqVIlnD59WqHjYxbau3cvevfuLfdHP2Xco1u3bqFnz54y44kr65kVevbsGc6fPy+3bvfp00ehseTl5WH69OlYunQpMjIyALwZZmXkyJEIDAxUyR8u6PPGpCkRERGRinr27Bl27NiBpKQkBAQEoGLFirh48SJMTU0VPrFQZmYmxo8fj3Xr1omzwGtoaGDAgAH49ddfZSb0UYTp06cjODgYTk5OqFq1qkxLxd27dys0njVr1uCnn35CpUqVUKVKFal4JBIJLl68qNB4inr9+jWSk5NRu3ZtqYlhFK1Pnz5IT0/H2rVrYWtri/j4eFhaWorjiV69elXhMZWUEJBIJOjdu7fC4zl69CgmTpyIGTNmoHHjxjLvLUW3NgsJCcH27dvx559/KnxSo8/F2bNn0atXL9y9exfFv14rK9nVtGlTaGho4Oeff5b7+aiMJHhWVhY2b96MixcvoqCgAI6OjvD19YWurq7CYwHeDGHw/fffY+rUqUoZ9qa4b775BhoaGhg1apTciaDc3NwUHtO+ffvg6+uLV69ewcDAQObv2pMnTxQaz9ChQ7F7924EBweLQ19ER0cjKCgIXl5eWLVqlULjoS8fk6ZEREREKighIQEtW7ZEhQoVcOfOHdy4cQOWlpaYOnUq7t69iw0bNiglrlevXiEpKQmCIMDKykopydJCVatWxbx585SS2JLH3Nwcw4YNw8SJE5UdiigzMxMjR44UWwLfvHkTlpaW8Pf3R7Vq1fDzzz8rNJ4qVargyJEjaNiwIQwMDMSkaXJyMuzt7cWWQ4qiagkBAFLdYIvGo6zWZg4ODrh9+zZyc3Nhbm4u855X1o8BqtTKvFGjRrC2tsb06dPlJiiLjnWqKOXKlUNsbCzq1q2r8HN/LgwMDBAXF4fatWsrOxQAgJ6eHi5dugQbGxtlhyKytrZG+/bt8csvv0jNWK8shoaG2LJlC9q1aydVfujQIfTo0QPPnz9XUmT0peJEUEREREQqaOzYsejXrx/mzZsnNUlGu3bt0KtXL6XFlZaWhrS0NKWPRQm8aT3p4uKilHPL8/TpU3Tv3l3ZYUiZNGkS4uPjERYWJo7XCQCtWrVCYGCgwpOmr169kvvF+9GjR9DW1lZoLAAwbtw49O/fX2USAoDqTZZVOBu7KnlbK3NFu3XrFnbs2KEyEwoBgJ2dHR49eqTsMERv+6FP0d28AaBLly44deqUyiRNnZyckJqaqlJJ03v37sHf319lPh91dHTkTtJnYWEBLS0txQdEXzy2NCUiIiJSQYaGhrh48SJq164t1SLv7t27sLGxQXZ2tkLjUcWxKCdOnAh9fX1MnTpV4eeWZ8CAAWjSpAmGDh2q7FBE5ubm2Lp1K7777jupenT79m04OjrixYsXCo2nQ4cOcHR0xIwZM8TJYMzNzdGjRw8UFBRgx44dCo2nXLlyuHz5MiwtLRV6XvowqtbKvEWLFpgwYYLUDxPKdvLkSUyZMgW//PIL7O3tlT6pkJGRkdR6bm4uMjMzoaWlBT09PaW06p41axYWL16MDh06yL1H/v7+Co1n+/btCAoKQkBAgNx4GjRooNB4gDeJ5R49esDb21vh55YnODgY169fR0hIiPhDW05ODgYMGIA6deqIk2YSfSxsaUpERESkgnR0dOQmtG7cuIHKlSsrPJ4xY8ZAU1MTKSkpsLW1Fct9fHwwZswYpSRNs7Oz8fvvv+P48eNo0KCBzBfMhQsXKjQeKysrTJ06FWfPnlWJL+AA8PDhQ7kTrLx69UoprfN+/fVXuLu748KFC3j9+jUmTJiAq1ev4smTJ4iMjFR4PJ6enrhw4YLKJU2fPXuGP/74A4mJiZBIJLCzs0P//v2V0s27UGxsrFQ8Dg4OSotF1VqZjxw5EuPGjcODBw9UJtnVqlUrAJCZUExZwzw8ffpUpuzWrVv46aefEBAQoNBYCq1duxb6+voIDw9HeHi41DaJRKLwz2wfHx8AQP/+/aXiUOZEUB06dEBAQACuXbsmt27/8MMPnzyGLl26SK0fP34cNWrUEMfljY+Px+vXr1Vm8jz6srClKREREZEKGjx4MB4+fIht27ahYsWKSEhIgLq6Ojp16gRXV1csXrxYofGo2liUAODh4VHiNolEgpMnTyowGqBWrVolbpNIJPjnn38UGM0bbm5u6NatG0aOHCm27KxVqxZGjBiB27dv4/DhwwqP6cGDB1i5ciViY2PFyWCGDx+OqlWrKjyWP/74A8HBwejXr5/SEgLFXbhwAZ6entDV1cU333wDQRBw4cIFZGVl4ejRo3B0dFRoPOnp6ejRowfCwsJQoUIFCIKA58+fw8PDA1u2bFHKjziq1sq86Di0hZSd7CqeBCxOGZMKyXPhwgX8+OOPuH79urJDUbq7d++Wut3c3FxBkfwfeXW7kKLqdr9+/cq8b0hIyCeMhL5GTJoSERERqaAXL16gffv2uHr1Kl6+fIlq1arhwYMHcHZ2xsGDBxU+AZOBgQEuXryIOnXqSCVNY2Ji0LZtWzx+/Fih8VDZREVFoW3btvD19cX69esxZMgQXL16FdHR0QgPD0fjxo2VHaJSqUJCoLjmzZvDysoKa9asgYbGm46BeXl5GDhwIP755x+cPn1aofH4+PggKSkJGzduFFuZX7t2DX379oWVlRU2b96s0HgAYNSoUdiwYQMaNGigEq3MVTHZ9bm4dOkS3NzcFD5USFGvX79GcnIyateuLb7niIgAJk2JiIiIVNrJkydx8eJFsUVeYZdLRVO1sSip7C5fvoz58+dLteycOHEi7O3tFR5LSEgI9PX1ZSbM2r59OzIzM9G3b1+Fx6RqdHV1cenSJZlZz69duwYnJydkZmYqNB5DQ0McP34cTZo0kSo/f/482rRpg2fPnik0HkD1WpmrqoiICKxevRr//PMPtm/fjurVq2Pjxo2oVasWmjVrptBY9u7dK7UuCALS0tLw22+/wczMDIcOHVJoPACQmZmJkSNHIjQ0FABw8+ZNWFpawt/fH9WqVVP4RHkAsHHjRqxatQrJycmIjo6Gubk5Fi9ejFq1asHLy0vh8RSVnZ0NHR0dpcYAvPkRKSwsDElJSejVqxcMDAxw//59lC9fHvr6+soOj74w/BmFiIiISIW1aNECLVq0UHYYKjcWJfAmcVLauJyKTpwUHYdOnnXr1ikoEmn29vZiUkDZ5syZg1WrVsmUm5iYYPDgwUya4s0EPSkpKTJJ09TUVBgYGCg8noKCApmWnACgqamJgoIChccDAKdOnVLKeUuiijPD79y5E71794avry8uXryInJwcAMDLly/xyy+/4ODBgwqNp1OnTlLrEokElStXRosWLZQyJjYATJo0CfHx8QgLC5OaxKtVq1YIDAxUeNJ05cqVmDZtGkaPHo1Zs2aJLd0rVKiAxYsXKyVpmp+fj19++QWrVq3Cf//9JyaWp06dCgsLCwwYMECh8dy9exdt27ZFSkoKcnJy0Lp1axgYGGDevHnIzs6W+/eF6EMwaUpERESkgoKDg0vdPm3aNAVF8oadnR0SEhKwcuVKqKur49WrV+jSpYvSxqIEgEaNGkmt5+bmIi4uDleuXFFK8q34RCe5ubm4cuUKnj17prTEd0pKSqnba9asqaBI3rh7967csV/Nzc3fGuunEh4ejvnz54uTHNna2iIgIADNmzdXSjw+Pj4YMGAA5s+fDxcXF0gkEpw5cwYBAQHo2bOnwuNp0aIFRo0ahc2bN6NatWoAgHv37mHMmDEqMfHKv//+C4lEgurVqysthlGjRkmtF58ZXhlJ05kzZ2LVqlXo06cPtmzZIpa7uLi89e/Lp6CsBHtp9uzZg61bt+K7776T+gHOzs4OSUlJCo9n2bJlWLNmDTp16oQ5c+aI5U5OThg/frzC4wGAWbNmITQ0FPPmzcOgQYPEcnt7eyxatEjhSdNRo0bByckJ8fHxMDY2Fss7d+6MgQMHKjQW+jowaUpERESkgnbv3i21npubi+TkZGhoaKB27doKT5oCbyaDmj59usLPW5JFixbJLQ8KClLKxFTFnxnwJlEwbNgwpc3ObmFhUWprXEWP2WliYoKEhARYWFhIlRf/Aqwof/75J/r164cuXbrA398fgiAgKioKLVu2xPr169GrVy+FxzR//nxIJBL06dMHeXl5AN606vzpp5+kEimK8ttvv8HLywsWFhYwMzODRCJBSkoK7O3t8eeffyo8HuDN+2rmzJlYsGCB+F43MDDAuHHjMHny5FLHqv0UVHFm+Bs3bsDV1VWmvHz58koZUqG4/Px8XL58Gebm5jAyMlJKDA8fPoSJiYlM+atXr0r93PxUkpOT4eDgIFOura2NV69eKTwe4E0r6t9//x0tW7bE0KFDxfIGDRooZfKuM2fOIDIyElpaWlLl5ubmuHfvnsLjoS8fk6ZEREREKujSpUsyZS9evICfnx86d+6s8HiaNm0KNzc3eHh4wMXFReETUb2LH3/8Ed988w3mz5+v7FCgpqaGMWPGwN3dHRMmTFD4+YvXo9zcXFy6dAkLFy7ErFmzFB5Pjx494O/vDwMDAzGhEx4ejlGjRqFHjx4Kj2fWrFmYN28exowZI5aNGjUKCxcuxIwZM5SSNNXS0sKSJUswe/ZsJCUlQRAEWFlZQU9PT+GxAICZmRkuXryIY8eO4fr16xAEAXZ2dkobXxkAJk+ejD/++ANz5sxB06ZNIQgCIiMjERQUhOzsbKXU7eLq1KmDOXPmKG1m+KpVq+L27dsyP1CcOXNGKT/ijB49Gvb29hgwYADy8/Ph6uqK6Oho6OnpYf/+/XB3d1d4TE2aNMGBAwcwcuRIABATpWvWrIGzs7PC46lVqxbi4uJkJg47dOgQ7OzsFB4P8KZVuZWVlUx5QUEBcnNzFR5PQUGB3B/7/v33X6UMX0JfPiZNiYiIiD4T5cuXR3BwML7//nv07t1boef+/vvvER4ejt9++w3Z2dlo3Lgx3Nzc4O7ujmbNmqnU5AvR0dEqMVlFoaSkJLHFoKI1bNhQpszJyQnVqlXDr7/+ii5duig0npkzZ+Lu3bto2bKlOEt1QUEB+vTpg19++UWhsQDAP//8g44dO8qU//DDD/jf//6n8HiK0tPTQ4UKFSCRSJSWMC2qdevWaN26tbLDAACEhoZi7dq1+OGHH8Syhg0bonr16hg2bJhKJE0BQF1dHffv31fKuYcMGYJRo0Zh3bp1kEgkuH//PqKjozF+/Hil9FTYsWMHfvzxRwDAvn37cOfOHVy/fh0bNmzA5MmTlTI29uzZs9G2bVtcu3YNeXl5WLJkCa5evYro6GiEh4crPJ6AgAAMHz4c2dnZEAQB58+fx+bNmzF79mysXbtW4fEAQL169RARESGTyN2+fbvcVrGfWuvWrbF48WL8/vvvAN4kujMyMhAYGIj27dsrPB768jFpSkRERPQZefbsGZ4/f67w806aNAmTJk1Cfn4+YmJiEBYWhrCwMCxcuBASiUScZESRiif8CmdjvnDhAqZOnarweMaOHSs3ngMHDqjcBEfW1taIiYlR+Hm1tLSwdetWzJgxA/Hx8dDV1YW9vb3MF3JFMTMzw4kTJ2RaUp04cQJmZmZKiSkvLw/Tp0/H0qVLxa7n+vr6GDlyJAIDA+VOyvSpnThxAosWLRLHfa1bty5Gjx6ttNamT548kZkoCwDq1q2LJ0+eKDye0maGb9q0qcLjAYAJEybg+fPn8PDwQHZ2NlxdXaGtrY3x48djxIgRCo/n0aNHqFKlCgDg4MGD6N69O6ytrTFgwAAsXbpU4fEAb8Z3jYyMxPz581G7dm0cPXoUjo6OiI6Ohr29vcLj6devH/Ly8jBhwgRkZmaiV69eqF69OpYsWaKUlvgAEBgYiN69e+PevXsoKCjArl27cOPGDWzYsAH79+9XeDyLFi2Ch4cH7OzskJ2djV69euHWrVuoVKkSNm/erPB46MsnEQRBUHYQRERERCSt+JfIwi/hGzduhKurq9K+HFy/fh3h4eEICwtDeHg4Xr9+jebNm8sdz/NT69evn9S6mpqaOBtzmzZtFB6Ph4dHifH0799fbFmpSC9evJBaL6xHQUFBuH79OuLi4hQekypZuXIlRo8ejf79+0tNurR+/XosWbIEQ4YMUXhMQ4cOxe7duxEcHCx2EY6OjkZQUBC8vLwUPjv0b7/9hjFjxqBbt25iPGfPnsWOHTuwcOFCpSTgvv32W3z77bcyn5MjR45ETEwMzp49q9B4io+hWnxmeGVNlgcAmZmZuHbtGgoKCmBnZ6e0XgHm5uZYs2YNWrZsiVq1amHFihX4/vvvcfXqVTRr1kzuuLBfs0ePHqGgoEDumKuKduTIEfzyyy+IjY1FQUEBHB0dMW3aNKX8nQWArKwsbN68GRcvXhTj8fX1ha6urlLioS8bk6ZEREREKqj4DONFE3CTJk1S+NhdPj4+OH36NAoKCuDq6gpXV1e4ubmhQYMGCo2D3o2amprMhCaCIMDMzAxbtmxRyLh9Y8eOxYwZM1CuXDmZ1rjFLVy48JPHU9zu3buxYMECJCYmAgBsbW0REBAALy8vhccCAIaGhtiyZQvatWsnVX7o0CH06NFD4S3Nq1evjkmTJskkR5cvX45Zs2Yppft5eHg4OnTogJo1a8LZ2RkSiQRRUVFITU3FwYMH0bx5c4XHRKULCgrC4sWLUbVqVWRmZuLmzZvQ1tbGunXrsGbNGkRHRyskjuI/JJWmfPnynzASIvocMGlKRERERG+lpqaGSpUqwc/PDx4eHmjevLnKjGMaGxsrdhu2s7NTyjhrRT18+BA3btyARCKBtbU1KleurLRYio/LV5h8t7KyUljLVw8PD+zevRsVKlSQaY1blEQiwcmTJxUSkyozNTVFWFgYbG1tpcoTExPh6uqKhw8fKjQeAwMDXLp0SWYIg1u3bsHBwUEcQkDR7t+/j+XLl0tNTjVs2DBUq1ZNKfEUKvx6rYzZ14vKzs7GsmXLcOrUKaSnp6OgoEBq+8WLFxUe044dO5Camoru3bujRo0aAN6MT1uhQgWF/Ugh74ek4gRBgEQikTvh0Mfm4OBQ5rqijGdWVEZGhkw9UkRiufjwF6UpOs4x0cfApCkRERHRZ+DFixc4efIkbGxsZJIpivDs2TOcPn1a7JZ/9epVNGzYEO7u7nB3d5dpFacI6enp6NGjB8LCwlChQgUIgiCO4bdlyxaFJytfvXqFkSNHYsOGDeIXS3V1dfTp0wfLli1Ticl8SPUFBwfj+vXrCAkJgba2NgAgJycHAwYMQJ06dRAYGKjQeHx9fdGoUSMEBARIlc+fPx+xsbEcR/D/27BhA3799VfcunULwJtxgwMCAhQ+aV+hXr164dixY+jWrRtMTU1lEnOKrkfyPHv2DBUqVFDoOd9lgic3N7dPGMkb06dPL/O+ynhmycnJGDFiBMLCwpCdnS2WKzKxLG/4i+JprML6rYh46OvCpCkRERGRCvL29oarqytGjBiBrKwsNGzYEHfu3IEgCNiyZQu6du2q1PiSkpIwc+ZM/PnnnygoKFDKFxUfHx8kJSVh48aNYiL52rVr6Nu3L6ysrBSezBkyZAiOHz8uNfnLmTNn4O/vj9atW2PlypUKjQd404qrUqVK6NChA4A3k8P8/vvvsLOzw+bNmxU+AVNoaCi6deuGcuXKKfS8RVWsWBE3b95EpUqVYGRkVGorL2VMKtS5c2ecOHEC2traaNiwIQAgPj4er1+/RsuWLaX23bVr1yePZ+bMmZg/fz6aNm0qNaZpZGQkxo0bJ9XSzN/f/5PFkZCQgPr160NNTQ0JCQml7qvoYUMWLlyIqVOnYsSIEWjatCkEQUBkZCSWL1+OmTNnYsyYMQqNB3gzzMPBgweVNhFVcXPnzoWFhQV8fHwAvPkbt3PnTlStWhUHDx7kUC8qysXFBQAwatQoucl3RSSWizp+/DgmTpyIX375RWpojilTpuCXX35B69atFRoPffmYNCUiIiJSQVWqVMGRI0fQsGFD/PXXXwgMDER8fDxCQ0Px+++/49KlSwqN58mTJ+IEUGFhYbh69SoqVqwIV1dXeHh4YPjw4QqNB3iTFDh+/DiaNGkiVX7+/Hm0adMGz549U2g8lSpVwo4dO+Du7i5VfurUKXh7eyu8WzUA2NjYYOXKlWjRogWio6PRsmVLLF68GPv374eGhoZCkm5FVa5cGZmZmejYsSN+/PFHtG3bVuETZIWGhqJHjx7Q1tbG+vXrS02a9u3bV4GRvVF8grPShISEfMJI3ig+vnJJJBIJ/vnnn08Wh5qaGh48eAATExOxi7W8r7KKav1WVK1atTB9+nT06dNHqjw0NBRBQUFITk5WaDwAYGdnhy1btqhMMtLS0hJ//vknXFxccOzYMXh7e2Pr1q3Ytm0bUlJScPToUaXE9fTpU/zxxx/iEC+2trbo168fKlasqJR4AODChQtS8TRu3Fhpsejr6yM2NhY2NjZKi6Go+vXrY9WqVWjWrJlUeUREBAYPHiyOTU30sSh+Ck8iIiIieqvnz5+LX9oOHz6Mrl27Qk9PDx06dJDpJqsIlStXRqVKldC8eXMMGjQI7u7uqF+/vsLjKKqgoACampoy5ZqamjLjrilCZmYmTE1NZcpNTEyQmZmp8HgAIDU1VRyLcs+ePejWrRsGDx6Mpk2byiR3FSEtLQ2HDx/G5s2b0aNHD+jq6qJ79+748ccfxRZNn1rRRKifn59CzvkuFJEIfRfKSPjJk5ycLA65oSoxFUpLS5Nbf11cXJCWlqaEiIAFCxZg4sSJWLVqlcJblMuTlpYGMzMzAMD+/fvh7e2NNm3awMLCAt9++61SYgoPD8cPP/wAQ0NDODk5AQCWLl2K4OBg7N27V+GtKP/991/07NkTkZGR4rAFz549g4uLCzZv3izeP0Vq0qQJUlNTVSZpmpSUBENDQ5lyQ0ND3LlzR/EB0RdP7e27EBEREZGimZmZITo6Gq9evcLhw4fRpk0bAG9axejo6Cg8nvj4ePz333/YsWMHRowYofSEKQC0aNECo0aNkpq9+969exgzZoxMN2ZFcHZ2RmBgoNS4b1lZWZg+fbpCZqmXR19fH48fPwYAHD16FK1atQIA6OjoICsrS+HxaGho4Pvvv8emTZuQnp6OxYsX4+7du/Dw8EDt2rUVHo+6ujrS09Nlyh8/fgx1dXWFx1MoLy8Px48fx+rVq/Hy5UsAbyY+UtakSwDw+vVr3LhxA3l5eUo5v7m5udgq+O7du6hevTrMzc2llurVq+Pu3bsKj83Kygrbtm2TKd+6dSvq1Kmj8HgAwMnJCdnZ2bC0tISBgQEqVqwotSiakZERUlNTAbz5IbDws0gQBKWNQzl8+HD4+PggOTkZu3btwq5du/DPP/+gR48eSuk90b9/f+Tm5iIxMRFPnjzBkydPkJiYCEEQMGDAAIXHAwBr167F3LlzERoaitjYWCQkJEgtitakSROMHj1a6seIBw8eYNy4cfjmm28UHg99+djSlIiIiEgFjR49Gr6+vtDX14e5ubnYKvD06dOwt7dXeDyFSVJVmhn+t99+g5eXFywsLGBmZgaJRIKUlBTY29vjzz//VHg8S5YsQdu2bVGjRg00bNgQEokEcXFx0NHRwZEjRxQeDwC0bt0aAwcOhIODA27evCmObXr16lVYWFgoJaZCenp68PT0xNOnT3H37l2ldKssaaSynJwcaGlpKTiaN+7evYu2bdsiJSUFOTk5aN26NQwMDDBv3jxkZ2dj1apVCo0nMzMTI0eORGhoKADg5s2bsLS0hL+/P6pVq4aff/5ZofEAgIeHB9LS0mBiYiJVXjgRnKKTcNOnT4ePjw9Onz6Npk2bQiKR4MyZMzhx4oTcZKoi9OzZE/fu3cMvv/widyxKRevSpQt69eqFOnXq4PHjx+LkgXFxcWJreEVLSkrCzp07pX4gUVdXx9ixY7FhwwaFxxMREYGoqCipVp02NjZYtmyZ0samffjwIZKSkqSGDSkcGkMZQ2GsW7cOnTt3hrm5OWrWrAkASElJgbW1Nfbs2aPQWOjrwKQpERERkQoaNmwYvvnmG6SmpqJ169bi7LGWlpaYOXOmwuNRxZnhzczMcPHiRRw7dgzXr1+HIAiws7MTWzApWv369XHr1i38+eefYjw9evSAr68vdHV1lRLT8uXLMWXKFKSmpmLnzp0wNjYGAMTGxqJnz55KiSkzMxO7d+/Gpk2bcPz4cZiZmaFnz57Yvn27wmJYunQpgDdf/teuXQt9fX1xW35+Pk6fPo26desqLJ6iRo0aBScnJ8THx4vPC3gzQdTAgQMVHs+kSZMQHx+PsLAwtG3bVixv1aoVAgMDlZI0LUzYFPf48WOlTDLWtWtXnDt3DosWLcKePXvEz6Lz58/DwcFB4fEAQFRUFKKjo8XJxJRt0aJFsLCwQGpqKubNmye+59LS0jBs2DClxOTo6IjExESZrueJiYlo1KiRwuOpWbMmcnNzZcrz8vJQvXp1hccDvGn96uDggM2bN6tE8t3KygoJCQly/+4rOzb6MnEiKCIiIqLPQH5+Pi5fvgxzc3MYGRkp/PyqODM8fX569uyJffv2QU9PD927d4evr6/CxjItqnByo7t376JGjRpSLc20tLRgYWGB4OBgpYy1WKlSJURGRsLGxgYGBgaIj4+HpaUl7ty5Azs7O4WPj2tubo6tW7fiu+++k4rn9u3bcHR0xIsXLxQWS5cuXQAAf//9N9q2bQttbW1xW35+PhISEmBjY4PDhw8rLCZV5ejoiBUrVuC7775TdigqpWiX8sTEREyYMAEjR44U79PZs2exfPlyzJkzB/+PvTuPqzH//8f/uEqptKII7WlTUrKVLUwIoRlb0WofMkmWMbaQNUJUtjYGSWYwiBaNxKRdWpQiQyURo0TL9fujX+fTcWJ8Pt/pdV1jXvfb7X275XXO7XY93s5xmvO8Xq/nc8aMGUSz/frrr/D19cXBgwfRv39/MAyD1NRULF26FKtWrcKUKVOI5gGATp06ISsri7PdwBTFNbrTlKIoiqIoiod++OEHmJiYwN3dHY2NjRgxYgSSk5MhIyODS5cuER/ic+7cOZHJ8La2tpCWlsb06dM5KZp6eHhAV1cXHh4eQusBAQEoKiqCv78/0Tzbtm1Dt27d4ObmJrR+/PhxVFZWYtWqVUTzAM29A2VlZQWThg8ePIgjR47AyMgIBw8eJF6AZxgGZ86cwdixY9GhA3dfRVoGCVlbWyM6OpqTGxGf0tTU1OaR1z///BNycnLE81RWVoocgwead5+T3tnVMgCGZVnIyckJ7eCWlJTE4MGDMW/ePKKZAODy5csQFxfH2LFjhdZjYmLQ1NQkOIpO0vbt2+Hl5YWtW7fCxMREZGievLx8u2e4cOECxo8fDwkJCVy4cOGzz7Wzs2v3PADQr18/wfHyFitXrhR5noODA5GiqZKSktC/o5qaGgwaNEjw+djQ0IAOHTrAzc2Nk6LpqFGjaNGU+k+jRVOKoiiKoigeioqKwuzZswEAFy9eRElJCfLz8xEeHo61a9fi1q1bRPPwcTL8uXPn2vwibmlpie3btxMvmgYHB+Pnn38WWe/Tpw9mzpzJSdHU29sbO3bsAADcu3cPXl5eWL58OeLj47F8+XKik9rr6+tRVlaG3r17c1owbS0hIYHrCCK++eYb+Pv74/DhwwCaC81v377Fhg0bYGtrSzzPgAED8Ntvv2Hp0qWCPABw5MgR4gPOWt6vmpqa8Pb25qQtSFtWr16N7du3i6yzLIvVq1dzUjRtaaXw8VA8kr0op0yZgvLycqioqHy24EeyN2bLDRO+IP176n9r0qRJ8PT0xL1799osvpMqdlMUV+jxfIqiKIqiKB6SkpJCUVERevXqhfnz50NGRgb+/v4oKSmBqakp0SOxQPMX7y5duiA8PBxSUlIAmifDOzs74+XLl4iNjSWaB2j+O8rJyRHZAVNUVARjY2OhKfak8uTl5QmOfrcoLi6GkZER8TwAICsri5ycHGhqamLjxo3IyclBVFQU0tPTYWtri/LycqJ5lJWVkZyczNlE8bb8+eefuHDhAkpLS/Hhwwehx/bs2UM8z9OnTzFq1CiIi4ujsLAQFhYWKCwsRNeuXfH777+3ueuzPSUnJ2PcuHFwdHREaGgoFixYgPv37+P27dtITExE//79ieYBmgtfDQ0NIu+jwsJCSEhIEB9yJi0tjby8PJHrPnr0CH369EFNTQ3RPACQmJj42cdHjBhBKAn1b9bST70tXAyCoijS+HGLl6IoiqIoihLSrVs35ObmQlVVFVevXsWhQ4cANO/4bN1/kRQ+TobX1dXF1atXsWTJEqH1K1euQFtbm3geNTU13Lp1S6RoeuvWLfTo0YN4HqD5yHLLTuDY2Fg4OTkBADp37ky88A4ATk5OOHbsWJu78rgQFxcHOzs7aGlpoaCgAMbGxnj06BFYloW5uTknmXr27InMzEycPn0aaWlpaGpqgru7O2cDxSwtLZGcnIxdu3ZBR0cH165dg7m5OW7fvg0TExPieQDAxcUFbm5uIkXTP/74A0ePHsWNGzeI5lFQUEBxcbFI0bSoqIiTwVQALYp+qYcPH8Lf3x95eXlgGAaGhoZYtmwZdHR0OMnT2NiIX375RZDHyMgIdnZ2nPzeByAY/EhR/1W0aEpRFEVRFMVDrq6umD59OlRVVcEwDL755hsAzUUBLqZ683Ey/PLly7FkyRJUVlZi1KhRAJqLYH5+fpwceZw7dy5++OEH1NfXC+VZuXIlvLy8iOcBgKFDh2L58uWwsrJCSkoKzpw5AwB48OABevXqRTzPhw8fcPToUVy/fh0WFhYiBSXSOzvXrFkDLy8v+Pj4QE5ODufOnYOKigocHR2FJsWTUl9fD319fVy6dAmurq5wdXUlnuHjPPPnz8e6desQFhbGaZbWMjIyBAPpWhs8eLDITRQS7Ozs8MMPP+D8+fOCYltRURG8vLw4Pb588+ZNBAcHo7i4GGfPnkXPnj0REREBLS0tQZ/j/7KYmBjY2dmhX79+sLKyAsuySE5ORp8+fXDx4kXB711SioqKYGtri6dPn0JfXx8sy+LBgwdQU1PDb7/9xlkht0VdXZ3gpAlXxMXFUVZWJrLjvqqqCioqKnTnK/WPo8fzKYqiKIqieCoqKgpPnjzBtGnTBAWusLAwKCoqYvLkyRyn44fAwEBs3boVz549AwDBMfSWHZUktfQv3L9/v+CYt5SUFFatWoV169YRH5oDAKWlpVi8eDGePHkCDw8PuLu7AwA8PT3R2NiI/fv3E81jbW39yccYhkF8fDzBNICcnBwyMzOho6MDJSUlJCUloU+fPsjKysLkyZPx6NEjonmA5p2msbGxMDQ0JH7ttigqKiI9PZ2T3dufoqCggBs3bsDMzExoPS0tDSNHjsRff/1FNM/r168xbtw4pKamCj6r//zzTwwbNgzR0dFQVFQkmgdo7vk8Z84cODo6IiIiArm5udDW1sahQ4dw6dIlXL58mXgmvjEzM8PYsWNFdr6vXr0a165dQ3p6OtE8tra2YFkWJ0+eROfOnQE0FwNnz54NMTEx/Pbbb0TzAM07X319fREUFISKigo8ePAA2traWLduHTQ1NQW/U0gRExMT9Mlt7dmzZ9DR0cG7d++I5qG+frRoSlEURVEURf3rVVZWQlpaGrKyslxHwdu3b5GXlwdpaWn07t0bHTt25DoS9Qndu3dHfHw8jIyM0KdPH2zbtg12dnbIysqClZUV3r59SzzT9u3bkZ+fj6NHj/JiYJarqytMTEywfPlyrqMITJw4ETIyMjh16pTg2HJjYyNmzJiBmpoaXLlyhXgmlmVx/fp1ZGVlQVpaGn379sXw4cOJ52hhZmYGT09PODk5QU5ODllZWdDW1kZmZibGjRtHvJ8xH0lJSeHevXsibR4ePHiAvn37Eu9D3alTJ9y5c0ek7QWXn0c+Pj4ICwuDj48P5s2bh5ycHGhrayMyMhJ79+7F7du3ieRoucHn6emJzZs3C/2ub2xsxO+//45Hjx4hIyODSB7qv4P738IURVEURVGUwJfu/PPw8GjnJPzXehiMsrKyYJ2rYTCvX79GY2MjOnfujAEDBgjWX758iQ4dOkBeXp5Yli/tV0oyU2tFRUV4+PAhhg8fDmlpacFEb9IGDx6MW7duwcjICBMmTICXlxfu3buH6OhoDB48mHgeoLkFR1xcHK5duwYTExORFgbR0dFE8+jq6mLz5s1ITk5G//79RfJw8Vm0c+dODB8+HPr6+hg2bBiA5qPob968Ib5buQXDMLCxsYGNjQ0n1/9YQUFBm0VbeXl5VFdXkw/EQ8rKysjMzBQpmmZmZhIfuAYAHTt2bHOX9Nu3byEpKUk8DwCEh4fj8OHDGD16NBYuXChY79u3L/Lz84nl2Lt3L4DmmxNBQUFCPV4lJSWhqamJoKAgYnmo/w5aNKUoiqIoiuKRli8Gn8MwDC2agn/DYGbOnIlJkyZh8eLFQuuRkZG4cOEC0eOwioqKny1CthQpSfd/q6qqwvTp05GQkACGYVBYWAhtbW3MnTsXioqK8PPzI5pnz549gt1bGzduxNu3b3HmzBno6up+0b/F9qCoqIhvv/2Wk2u35ejRo1BUVERaWhrS0tKEHuPqs8jIyAjZ2dkICAgQ7Ox0cnLCkiVLBMeaSfLw8ICurq7I30VAQACKioo46bGsqqqKoqIikZtHSUlJnLRa+NSNHIZh0LFjR06KgvPmzcP8+fNRXFwMS0tLMAyDpKQk7Nixg5M+1BMnTsT8+fNx7NgxDBw4EEDz77OFCxdy1hv36dOn0NXVFVlvampCfX09sRwlJSUAmlu8REdHQ0lJidi1qf82ejyfoiiKoiiK+leSl5dHenq6yBe6oqIiWFhYEN9N1blzZ9y6dUukF2V+fj6srKxQVVVFLEtiYuIXPY/0hG0nJyc8f/4cR48ehaGhoeDI8LVr1+Dp6Yn79+8TzUN9XaqqqhAREYEffviB6HV79uyJCxcuoH///kLr6enpsLOzw59//kk0D9C8GzcsLAzHjx/HN998g8uXL+Px48fw9PTE+vXriQ/MEhMT++yNnF69esHFxQUbNmyAmJgYkUwsy8Lf3x9+fn6Cvtg9evSAt7c3PDw8iO9+r66uhrOzMy5evAgJCQkAQENDA+zs7BAaGgoFBQWieQDAwsICP/zwA2bPni3U5mHTpk2IjY3FzZs3iWeiKJLoTlOKoiiKoijqb2lra+Pu3bvo0qWL0Hp1dTXMzc1RXFxMPBPDMG0eZWw5Jk/a+/fv0dDQILJeX19PfDgF6WLol7p27RpiYmIEw3Ja9O7dG48fPyaeh4/va+p/h2VZXLt2DceOHcOvv/4KeXl54kXTqqqqNgta8vLyePHiBdEsLVauXInXr1/D2toadXV1GD58ODp27IgVK1YQL5gCQGhoKNauXQsXFxcMHDgQLMvi7t27CAsLw08//YTKykrs3r0bHTt2xI8//tjueRoaGnDy5EnMmjULnp6egt8lcnJy7X7ttrAsi9evX+PUqVN49uwZ8vLywLIsjIyM2tzpScqGDRswZ84cPH36FE1NTYiOjkZBQQHCw8Nx6dIl4nnc3Nw++/jx48cJJaH+K2jRlKIoiqIoivpbjx49arMQ+f79ezx9+pSDRMCwYcOwbds2kWEw27Ztw9ChQ4nnGTBgAA4fPowDBw4IrQcFBYnsQPuvqqmpgYyMjMj6ixcvOBmYxcf3NfVlHj16hOPHjyM0NBRPnz6Fg4MDfvvtN1hbWxPPoquri6tXr4oUI69cucLJUfgWW7duxdq1a5Gbm4umpiYYGRlxNiwvLCwMfn5+mD59umDNzs4OJiYmCA4ORlxcHNTV1bF161YiRdMOHTpg0aJFyMvLA8BdsbQFy7Lo3bs37t+/j969e3NaKG1t0qRJOHPmDHx9fcEwDNavXw9zc3NcvHgR33zzDfE8r169EvpzfX09cnJyUF1djVGjRhHPQ339aNGUoiiKoiiK+qQLFy4Ifo6JiRHaTdXY2Ii4uDjiA5da8G0YzNatWzFmzBhkZWVh9OjRAIC4uDjcvXsX165dI56Hj4YPH47w8HBs3rwZQPNu4aamJuzatYtosYvP72vq096/f4/o6GgcPXoUycnJGD9+PPbs2YNZs2ZhzZo1MDIy4iTX8uXLsWTJElRWVgoKN3FxcfDz8+Okn2lrMjIysLCw4DQDANy+fbvNQT1mZmaCCexDhw5FaWkpsUyDBg1CRkYGNDQ0iF3zU8TExNC7d29UVVWJ9Onm2tixYzF27FiuYwAAzp8/L7LW1NSExYsXc3qDgvp60Z6mFEVRFEVR1Ce19JZjGAYf/2djy4R6Pz8/TJw4kYt4ePbsmdAwmL59+3I2DAZonrq8a9cuZGZmCvKsWbOGd1+CuZKbm4uRI0eif//+iI+Ph52dHe7fv4+XL1/i1q1b0NHRIZKD7+9rqm1du3aFkZERZs+ejWnTpgmGwUhISCArK4uzoikABAYGYuvWrYLemJqamti4cSOcnJw4yTN16tQ2e3IyDAMpKSno6urCwcEB+vr6RPLo6enB3t4e27dvF1pfvXo1zp8/j4KCAqSmpmLy5MnEdnmfPXsWq1evhqenJ/r3749OnToJPd63b18iOVr89ttv2L59OwIDA2FsbEz02p/yb2lhUlBQgJEjR6KsrIzrKNRXhhZNKYqiKIqiqL+lpaWFu3fvomvXrlxHof7lysvLERgYiLS0NDQ1NcHc3Bzff/89VFVViWfh2/u6vr4eNjY2CA4Ohp6eHtdx0NDQgK1bt8LNzQ1qampcx4GSkhL69u2L2bNnY8aMGZCXlwfAj6Jpi8rKSkhLS3N2DL6Fi4sLfvnlFygqKqJ///5gWRYZGRmorq6GjY0NsrKy8OjRI8TFxcHKyqrd81y4cAHTpk2DgYEBBgwYAIZhcPfuXeTn5yMqKgoTJ05EYGAgCgsLsWfPnnbPA6DNgVMtN1IYhiHeG1tJSQm1tbVoaGiApKQkpKWlhR5/+fIl0TxA899ReXk5VFRUhNYrKiqgrq6O9+/fE8/UlsuXL8PZ2RmVlZVcR6G+MrRoSlEURVEUxVNNTU0oKirC8+fP0dTUJPTY8OHDOUrFP7W1tSgtLcWHDx+E1knvEmrt3bt3qK+vF1prKfBQ1OcoKysjOTmZN7uTZWVlkZOTw4t2BXV1dTh37hyOHTuGO3fuYPz48YICamZmJi+KpnyxevVqvHnzBgEBAYLiYFNTE5YtWwY5OTls3boVCxcuxP3795GUlEQk06NHjxAUFIQHDx6AZVkYGBhgwYIFnL23/m74HOlj+2FhYZ993NnZmVCS/2lhMmXKFISFhbXZwuT69esoKCgglgloboXRGsuyKCsrw2+//QZnZ2cEBAQQzUN9/WjRlKIoiqIoiofu3LkDBwcHPH78WOT4MBc7YAAgMTERu3fvRl5eHhiGgaGhIby9vQX9REmrrKyEq6srrly50ubjpP+OamtrsXLlSkRGRqKqqorzPAD/juhevXoVsrKygkFdBw8exJEjR2BkZISDBw8KjluTFBcXh7i4uDZvTnAxidnLywsSEhIix5i5MmXKFEyZMgUuLi5cRxHy8OFDhISEICwsDE+fPsWsWbPg4uKCUaNGCQbDkRQVFYXIyMg2b+Ckp6cTz6OsrIxbt26J7Fh+8OABLC0t8eLFC9y7dw/Dhg1DdXU18XwUv/G1hcnHva/FxMSgrKyMUaNGwc3NDR060LE91D+LvqMoiqIoiqJ4aOHChbCwsMBvv/0GVVXVNgtfJJ04cQKurq6wt7eHh4cHWJZFcnIyRo8ejdDQUDg4OBDP9MMPP+DVq1e4c+cOrK2tcf78eVRUVGDLli3w8/Mjnsfb2xsJCQk4dOgQnJyccPDgQTx9+hTBwcGcFcAUFBQ+e0T3zJkz2LFjB7Ejut7e3tixYwcA4N69e1i+fDm8vLwQHx+P5cuXIyQkpN0ztLZp0yb4+PjAwsKCF//OAODDhw84evQorl+/DgsLC5E+i6SOLrcYP3481qxZg5ycnDb7PtrZ2RHN00JHRwdbtmyBj48PYmJicOzYMUycOBFycnJ48eIF0Sz79+/H2rVr4ezsjF9//RWurq54+PAh7t69i++//55olhYNDQ3Iz88XKZrm5+cLbuBISUkRfc9XV1cjJSWlzRsUXPV+LSgowIEDBwQ3Aw0MDLB06VJiN5I+1tjYiPPnzwvdnJw8eTLxYmDL68O3FiYJCQlcR6D+Y+hOU4qiKIqiKB7q1KkTsrKyoKury3UUAIChoSHmz58PT09PofU9e/bgyJEjyMvLI55JVVUVv/76KwYOHAh5eXmkpqZCT08PFy5cwM6dO4kdOW2hrq6O8PBwjBw5EvLy8khPT4euri4iIiJw6tQpXL58mWgegH9HdFsf9d64cSNycnIQFRWF9PR02Nraory8vN0ztKaqqoqdO3dizpw5RK/7OR/vpGqNYRjEx8cTTNN238cWXO16/5TKykpERESIHOFtbwYGBtiwYQNmzZoFOTk5ZGVlQVtbG+vXr8fLly85OTLs4eGBU6dO4ccffxT0EE1JSYGvry8cHBywb98+HD16FKGhoUT+7V+8eBGOjo6oqamBnJycULGWYRhO+nVGRUVh1qxZsLCwwJAhQwA0n/K4e/cufv75Z0ybNo1onpycHEyePBnl5eWCou2DBw+grKyMCxcuwMTEhGgePqusrERBQQEYhoGenh6UlZW5jkR9pWjRlKIoiqIoiodGjRqFlStXYty4cVxHAQB07NgR9+/fFyniFhUVwdjYGHV1dcQzycvLIzs7G5qamtDU1MTJkydhZWWFkpIS9OnTB7W1tUTzyMrK4v79+9DQ0ECvXr0QHR2NgQMHoqSkBCYmJnj79i3RPAD/juh27twZSUlJMDIywtChQ+Hk5IT58+fj0aNHMDIyIv6adenSBSkpKdDR0SF6XerrIiMjg7y8PGhoaEBFRQXXr1+HqakpCgsLMXjw4DbbdbS3xsZGbN++HQEBAaioqAAAdOvWDUuXLsWqVasgLi6O0tJSiImJoVevXu2eR09PD7a2tvD19YWMjEy7X+9LaGtrY/bs2fDx8RFa37BhAyIiIohPhh88eDBUVFQQFhYmaFXy6tUruLi44Pnz57h9+zbRPC1qamqQmJjYZusJDw8P4lmWLl2K8PBwwW5YcXFxODk54cCBA7x5b1FfD3o8n6IoiqIoioeWLl0KLy8vlJeXw8TEBBISEkKPkx5ypKamhri4OJGiaVxcHGdTtfX19VFQUABNTU3069cPwcHB0NTURFBQECeT2LW1tfHo0SNoaGjAyMgIkZGRGDhwIC5evAhFRUXieQD+HdEdOnQoli9fDisrK6SkpODMmTMAmou4JAo3H5s7dy5+/vlnrFu3jvi1qa9H9+7dUVVVBQ0NDWhoaODOnTswNTVFSUmJSD9IUsTFxbF27VqsXbsWb968ASA6jE5dXZ1YnqdPn8LDw4NXRa3y8vI22wLMnj0bu3btIp4nKysLqampQr2dlZSUsHXrVgwYMIB4HgDIyMiAra0tamtrUVNTg86dO+PFixeQkZGBiooK8aLp8uXLkZiYiIsXLwpayiQlJcHDwwNeXl4IDAwkmof6+tGiKUVRFEVRFA99++23AAA3NzfBWstABi6OxHp5ecHDwwOZmZmwtLQEwzBISkpCaGgo9u3bRzRLix9++AFlZWUAmncGjR07FidPnoSkpCRCQ0OJ53F1dUVWVhZGjBiBNWvWYMKECThw4AAaGhqI96FsMWfOHLi7u7d5RLelWJCYmIg+ffoQyRMQEIDFixcjKioKgYGB6NmzJwDgypUrnOyqrqurw+HDhxEbG4u+ffuK3Jzg4nWztrb+bBGb9PH8j3fhfWz9+vWEkvDXqFGjcPHiRZibm8Pd3R2enp6IiopCamoq7O3tuY4nUizlwtixY5GamgptbW2uowiMHDkSN2/eFLkZmJSUxMmAQ319fVRUVIh8Hj9//pyzVj2enp6YNGkSAgMDoaioiDt37kBCQgKzZ8/GsmXLiOc5d+4coqKiMHLkSMGara0tpKWlMX36dFo0pf5x9Hg+RVEURVEUDz1+/Pizj2toaBBK8j/Onz8PPz8/Qf9SQ0NDeHt7Y/LkycSztKW2thb5+flQV1fnxdCK0tJSpKamQkdHB6amppxk4NsRXb7hW/9QACJ9g+vr65GZmYmcnBw4OzsTv0lhZmYmkqekpAQdOnSAjo4OJ5Ph+aapqQlNTU2CYT2RkZFISkqCrq4uFi5cCElJSU5yRUVFITIyss1j1aRft2PHjsHHxweurq5tnp7gYqBYUFAQ1q9fj+nTp2Pw4MEAmnuanj17Fps2bUKPHj2I5rt8+TJWrlyJjRs3CuXx8fHB9u3bMXToUMFzSRXCFRUV8ccff0BfXx+Kioq4ffs2DA0N8ccff8DZ2Rn5+flEcrSQkZFBWloaDA0Nhdbv37+PgQMHoqamhmge6utHi6YURVEURVEU9R/wqSO61L/Dxo0b8fbtW+zevZvrKHjz5g1cXFwwdepUToZo+fj4YMWKFSJHvd+9e4ddu3bR3a8A9u/fj7Vr18LZ2RlHjhyBq6srHj58iLt37+L777/H1q1biebh40Cxz2VqjVS+1nladpu3lGta/5nk31frvtj6+vrYv38/xo4di/z8fJibmxPvQz169Gh06dIF4eHhkJKSAtD8797Z2RkvX75EbGws0TzU148WTSmKoiiKonjq4cOH8Pf3R15eHhiGgaGhIZYtW8bp0JoPHz7g+fPnggEMLUj2xqOof1pRUREePnyI4cOHQ1paWlCY4JOioiIMHDiQkynjbcnJycHEiRPx6NEj4tcWFxdHWVkZVFRUhNarqqqgoqLCSQGObwwMDLBhwwbMmjULcnJyyMrKgra2NtavX4+XL18iICCA64jURxITE7/4uSNGjGjHJP/DxsYGLi4ucHBwwMKFC5GRkQEPDw9ERETg1atX+OOPP4jkaJGTk4Nx48ahrq4OpqamYBgGmZmZkJKSQkxMDLFWM9R/B+1pSlEURVEUxUMxMTGws7NDv379YGVlBZZlkZycjD59+uDixYv45ptviOYpLCyEm5sbkpOThda56rFKfZmKigqsWLECcXFxeP78uchQmv/661ZVVYXp06cjISEBDMOgsLAQ2tramDt3LhQVFeHn58d1RIHbt28LdlbxQXV1NV6/fs3JtT9V1M7KykLnzp05SMQ/paWlsLS0BABIS0vjr7/+AtDc53jw4MG0aMpDpAqh/xu+vr6C987mzZvh7OyMRYsWQVdXF8ePHyeex9jYGIWFhThx4gTy8/PBsixmzpwJR0dHSEtLE89Dff1o0ZSiKIqiKIqHVq9eDU9PT2zfvl1kfdWqVcSLpi4uLujQoQMuXboEVVVV3u3Co9rm4uKC0tJSrFu3jr5ubfD09ISEhARKS0uFeuTNmDEDnp6enBRNPx4cxLIsysrKkJqainXr1hHPs3///jbzREREEB/epaSkBIZhwDAM9PT0hN7PjY2NePv2LRYuXEg0E191794dVVVV0NDQgIaGBu7cuQNTU1OUlJSI3DxpL/v378f8+fMhJSUl8j76GOkp7NSXsbCwEPysrKyMy5cvc5immbS0NObNm8d1DOo/gh7PpyiKoiiK4iEpKSncu3cPvXv3Flp/8OAB+vbti7q6OqJ5OnXqhLS0NBgYGBC97r9FQ0MDTp48ibFjx6J79+5cxxGQk5PDzZs30a9fP66joKGhAVJSUsjMzISxsTHXcQA0F5ZiYmJgamoqdIS5pKQEJiYmePv2LfFMrq6uQn8WExODsrIyRo0aBRsbG+J5tLS0PplnzZo1kJOTI5YlLCwMLMvCzc0N/v7+UFBQEDwmKSkJTU1NDBkyhFgePps7dy7U1NSwYcMGBAUFYfny5bCyskJqairs7e1x7Nixds+gpaWF1NRUdOnSReR91BrDMCguLm73PNT/3qhRoxAdHQ1FRUWh9Tdv3mDKlCmcDMsrKCjAgQMHBK2LDAwMsGTJEvrfJ1S7oDtNKYqiKIqieEhZWRmZmZkiRdPMzEyRPn4kGBkZ4cWLF8Sv+3eqq6uRkpLSZp9VJycnYjk6dOiARYsWIS8vj9g1v4SamhqxXWV/p0OHDtDQ0OBVS4CamhqRYUIA8OLFC3Ts2JGDREBISAgn1/2UkpISriMIODs7A2guxllaWopMYKf+x+HDhwWfiQsXLkTnzp2RlJSESZMmEduN2/q9w6f3EfXlbty4gQ8fPois19XV4ebNm8TzREVFYdasWbCwsBDcILlz5w5MTEzw888/Y9q0acQzUV83utOUoiiKoiiKh3x8fLB3716sXr0alpaWYBgGSUlJ2LFjB7y8vPDTTz8RzRMfH4+ffvoJvr6+MDExESlWcDGR/eLFi3B0dERNTQ3k5OSEjuoyDEN8YI61tTV++OEHTJ48meh1P+fatWvw8/NDcHAwNDU1uY6DkJAQnD17FidOnOBF78kJEybA3NwcmzdvhpycHLKzs6GhoYGZM2eiqakJUVFRnGVLS0sT7KQyMjKCmZkZZ1la/Pnnn2AYBj179uQ6isC7d+9QX18vtEb684j2Dv57Pj4+WLFihchNinfv3mHXrl1Yv349R8motmRnZwMA+vXrh/j4eKHP68bGRly9ehXBwcHEB8Fpa2tj9uzZ8PHxEVrfsGEDIiIi6I5l6h9Hi6YURVEURVE8xLIs/P394efnh2fPngEAevToAW9vb3h4eBDvTSkmJgYAItflchCUnp4ebG1t4evr2+ZuQdLOnj0r6EXbv39/dOrUSejxvn37Es+kpKSE2tpaNDQ0QEZGRqTYTbqwbGZmhqKiItTX10NDQ0Pk7yg9PZ1ontzcXIwcORL9+/dHfHw87OzscP/+fbx8+RK3bt2Cjo4O0TwA8Pz5c8ycORM3btyAoqIiWJbF69evYW1tjdOnT0NZWZlonqamJmzZsgV+fn6CdgVycnLw8vLC2rVrBZ8NJNXW1mLlypWIjIxEVVWVyOOkP4/Gjx+P0tJSLFmypM3ewVzdSOHLTnwAEBcXR1lZmchJiaqqKqioqHDyO+TNmzdtrjMMg44dO0JSUpJwIv4QExMTvI/bKhlJS0vjwIEDcHNzI5pLRkYG2dnZ0NXVFVovLCyEqakpamtrieahvn70eD5FURRFURQPMQwDT09PeHp6CibXkuwd+LGEhATOrv0pT58+hYeHBy8KpkDz8CBAeKAJwzCcFpb9/f2JX/NzpkyZwnUEIUZGRsjOzkZgYCDExcVRU1MDe3t7fP/991BVVeUk09KlS/HmzRvcv39fMJwqNzcXzs7O8PDwwKlTp4jmWbt2LY4dO4bt27fDysoKLMvi1q1b2LhxI+rq6rB161aieQDA29sbCQkJOHToEJycnHDw4EE8ffoUwcHBIsPzSEhKSuJN7+AWf7cTn3TRtOVz8GNZWVmc7TpXVFT87A3IXr16wcXFBRs2bCByc8DMzKzNPAzDQEpKCrq6unBxcYG1tXW7Z2kZGKatrY2UlBShmzWSkpJQUVGBuLh4u+f42MiRI3Hz5k2RomlSUhKGDRtGPA/19aM7TSmKoiiKoqh/JXt7e8ycORPTp0/nOgoA4PHjx599XENDg1AS6t9MQUEBsbGxGDBggNB6SkoKbGxsUF1dTTRPjx49EBQUBDs7O6H1X3/9FYsXL8bTp0+J5gEAdXV1hIeHY+TIkZCXl0d6ejp0dXURERGBU6dOEZ/wbWRkhJMnT/KihUILvuzEV1JSAsMweP36NeTl5YWKgo2NjXj79i0WLlyIgwcPEs8WHh6OtWvXwsXFBQMHDgTLsrh79y7CwsLw008/obKyErt374a3tzd+/PHHds+zZs0aBAYGwsTERJAnNTUV2dnZcHFxQW5uLuLi4hAdHc2rNjDt7cKFC4Kfnz17hvXr12P69OkYPHgwgOaepmfPnsWmTZuI9eul/jto0ZSiKIqiKIonzM3NERcXByUlpU/uOGlB+hgzHx07dgw+Pj5wdXVts8/qx0We/4o3b94Iejp+6vhpCy560QL86dd59epVyMrKYujQoQCAgwcP4siRIzAyMsLBgwehpKREPJOcnFybuxYzMjIwYsSIv31N/2lSUlLIzs6Gnp6e0HpBQQH69euHd+/eEc0DALKysrh//z40NDTQq1cvREdHY+DAgSgpKYGJiYmgjQApfOsdDACdOnXCvXv3oK2tzWmOsLAwsCwLNzc3+Pv7Q0FBQfCYpKQkNDU1BQN9SBs9ejQWLFggcuMtMjISwcHBiIuLQ0REBLZu3Yr8/Px2zzNv3jyoq6tj3bp1QutbtmzB48ePceTIEWzYsAG//fYbUlNT2z0P0Pz6de3aFRMmTAAArFy5EocPH4aRkRFOnTpF5Gbgl+7y5epEB/V1o0VTiqIoiqIonti0aRO8vb0hIyODjRs3frZoumHDBoLJ+OlzX6S4+PIUHh7+2cdJHYdt3TuwdV+61rhqGcC3fp0mJibYsWMHbG1tce/ePVhYWMDLywvx8fEwNDTkZJL95MmTUV1djVOnTqFHjx4AmltRODo6QklJCefPnyeaZ9CgQRg0aBD2798vtL506VLcvXsXd+7cIZoHaO4PfODAAYwYMQI2Njbo27cvdu/ejf3792Pnzp34888/iebhW+9ggH878RMTE2FlZYUOHfjTIVBGRgZZWVno3bu30Hrr/pglJSXo06cPkV6ZCgoKSEtLEzl6XlRUhP79++P169fIz8/HgAEDBG172pu+vj4CAwMxatQo3L59G6NHj4a/vz8uXbqEDh06IDo6mkgOiuIKfz6xKIqiKIqi/uNaF0I3btzIXZB/iY8Hm3Bt2bJlQn+ur69HbW0tJCUlISMjQ6xo2nrSMd960fKtX2dJSQmMjIwAAOfOncOkSZPg6+uL9PR02NraEs3SIiAgAJMnT4ampibU1NTAMAxKS0thYmKCEydOEM+zc+dOTJgwAbGxsRgyZAgYhkFycjKePHlC/Bh8C1dXV2RlZWHEiBFYs2YNJkyYgAMHDqChoQF79uwhnodvvYMBYMKECfD29kZubi4vduLLyckhLy8PJiYmAJrbO4SEhMDIyAgbN27kZOhSr169BP16Wzt27BjU1NQANA+qIrXjXEpKCsnJySJF0+TkZEhJSQFo/r3XsWNHInkA4MmTJ4I8v/zyC7777jvMnz8fVlZWGDlyJLEcFMUVWjSlKIqiKIriIW1tbdy9exddunQRWq+uroa5uTmKi4s5SkZ9yqtXr0TWCgsLsWjRInh7exPLMWLECABAQ0MDbty4ATc3N0EBgGtXr15FbGysoGAKQHAU3sbGhngeSUlJwQ6y2NhYQWG7c+fOxI/Bt1BTU0N6ejquX7+O/Px8sCwLIyMjjBkzhpM8I0aMQEFBAQ4dOiTIY29vj8WLFwt2wpLm6ekp+Nna2hr5+flITU2Fjo4OTE1NiedxdnYmfs2/M2/ePACAj4+PyGNc7DJfsGABVq9eDRMTExQXF2PGjBmwt7fH2bNnUVtby0nheffu3Zg2bRquXLmCAQMGgGEY3L17F/n5+YiKigIA3L17VzDkr70tXboUCxcuRFpamiBPSkoKjh49KuipGhMTQ7SdiaysLKqqqqCuro5r164J/u1JSUlx0poDAGpqapCYmIjS0lJ8+PBB6LHWgxgp6p9Aj+dTFEVRFEXxkJiYGMrLy6GioiK0XlFRATU1NZEvCu1t1KhRiI6OhqKiotD6mzdvMGXKFMTHxxPN0+Lf8OUpNTUVs2fPJtIT72NycnK4d+8eb/os8q1fp52dHT58+AArKyts3rwZJSUl6NmzJ65du4YlS5bgwYMHRPMAzbtftbS0iF/336qurk6wC48P3r17h/r6eqE1rnoH84mCggLS09Oho6ODHTt2ID4+HjExMbh16xZmzpyJJ0+ecJLr0aNHCAoKwoMHD8CyLAwMDLBgwQLOPjNPnjyJgIAAFBQUAGg+Hr906VI4ODgAaH5/MQxD7D3v6OiI/Px8mJmZ4dSpUygtLUWXLl1w4cIF/Pjjj8jJySGSo0VGRgZsbW1RW1uLmpoadO7cGS9evICMjAxUVFToDWXqH0d3mlIURVEURfFI6ymxMTExQkMzGhsbERcXx0lB5caNG20Wauvq6nDz5k3ieYC///LEl6KpuLg4nj17xsm1R48ejRs3bsDFxYWT639s1KhRWLZsmUi/Tk9PT4wePZp4noCAACxevBhRUVEIDAxEz549AQBXrlzBuHHjiOcBAF1dXQwfPhzu7u747rvvOC8Iampqws3NDa6urrzZsdzY2AhfX18EBQWhoqICDx48gLa2NtatWwdNTU24u7sTzVNTU4NVq1YhMjISVVVVbeb9r2NZVtBSJTY2FhMnTgTQvLP6xYsXnOXS1NQUOZ7PJUdHRzg6On7ycWlpaYJpmofj/fTTT3jy5AnOnTsnOP2SlpaGWbNmEc0CNO8ynzRpEgIDA6GoqIg7d+5AQkICs2fPFmmRQ1H/BLrTlKIoiqIoikdahhsxDIOP/zNNQkICmpqa8PPzE3zhbG/Z2dkAgH79+gn1ygSaCwFXr15FcHAwHj16RCRPayNHjoSenp7gy1NWVpbQlyd7e3uieVoXvIHmIkFZWRkCAgKgpqaGK1euEM0DAMHBwdi4cSMcHR3Rv39/dOrUSehx0n0Nnzx5gsmTJyMnJ0ekX+evv/6KXr16Ec3DRzk5OTh+/DhOnjyJ9+/fY8aMGXBzc8OgQYM4yXPgwAGEhoYiKysL1tbWcHd3x9SpU4n2VfyYj48PwsLC4OPjg3nz5iEnJwfa2tqIjIzE3r17cfv2baJ5vv/+eyQkJMDHxwdOTk44ePAgnj59iuDgYGzfvv2zRbD2xKed+KNGjYKamhrGjBkDd3d35ObmQldXF4mJiXB2dubkdwjQ3PImJSUFz58/F+mTTaoP9cc+fPjQZh51dXVO8vCJoqIi/vjjD+jr60NRURG3b9+GoaEh/vjjDzg7O3NyooP6yrEURVEURVEU72hqarKVlZVcx2AZhmHFxMRYMTExlmEYkf/JyMiwx44d4ySbgoICm5+fL/g5NzeXZVmWvXPnDquvr088z8d/N2JiYmy3bt3YWbNmsc+ePSOep61MH+fjyrVr19j9+/ez+/btY69fv85ZDpZl2aKiInbt2rXszJkz2YqKCpZlWfbKlStsTk4Op7nq6+vZ6Oho1s7OjpWQkGCNjIxYPz8/9vnz55zkyczMZD08PFhlZWVWSUmJ/f7779m0tDROsujo6LCxsbEsy7KsrKws+/DhQ5ZlWTYvL49VVFQknkdNTY1NSEhgWZZl5eTk2MLCQpZlWTY8PJwdP3488Twsy7Lp6els9+7dWXl5eVZcXJxVVlZmGYZhO3XqxGppaRHPk5WVxRobG7Py8vLsxo0bBetLlixhZ82aRTwPy7LshQsXWDk5OVZMTIxVUFBgFRUVBf9TUlIinufBgwfs0KFDBb9zW//u5fLzmk+6du3KFhQUsCzLsnp6euzVq1dZlm3+ty8tLc1lNOorRXeaUhRFURRFUZ/0+PFjsCwLbW1tpKSkQFlZWfCYpKQkVFRUIC4uzkk2ZWVl3Lp1C3p6etDX18f+/fsxduxY5Ofnw9zcXDDgh6I+JTExEePHj4eVlRV+//135OXlQVtbGzt37kRKSopgGAyX3r9/j0OHDmHNmjX48OEDJCQkMGPGDOzYsQOqqqrE89TX1+PQoUNYtWoV6uvrYWxsjGXLlsHV1RUMwxDJIC0tjfz8fGhoaEBOTg5ZWVnQ1tZGbm4uBg4ciLdv3xLJ0UJWVhb379+HhoYGevXqhejoaAwcOBAlJSUwMTEhngfg3078T6mrq4O4uDgkJCSIX1tPTw+2trbw9fWFjIwM8et/zMrKCh06dMDq1auhqqoq8u+JiyFnfGNjYwMXFxc4ODhg4cKFyMjIgIeHByIiIvDq1Sv88ccfXEekvjK0pylFURRFURQPeXh4QFdXV+QIZUBAAIqKiohNGtbQ0AAAkWOCfGBmZobU1FTo6enB2toa69evx4sXLxAREQETExNOs7XsSyBVRKL+b1avXo0tW7Zg+fLlkJOTE6xbW1tj3759HCZrHiB2/PhxnD59Gp06dcKKFSvg7u6OZ8+eYf369Zg8eTJSUlKI5amvr8f58+cREhKC69evY/DgwYI8a9euRWxsLH7++WciWfr06YObN28KPp9anD17luhk8Rba2tp49OgRNDQ0YGRkhMjISAwcOBAXL14UGZ5HSmZmJoKDgyEuLg5xcXG8f/9ecEPA2dmZN0VTLnv2Pn36FB4eHrwomALNr1laWhoMDAy4jsJbvr6++OuvvwAAmzdvhrOzMxYtWgRdXV2EhIRwnI76GtGiKUVRFEVRFA+dO3dOpEcmAFhaWmL79u3EiqatFRQU4MCBA8jLywPDMDAwMMCSJUs4+4LHxy9P4eHh2LVrFwoLCwE072Ty9vbGnDlzOMkDNO+m3L17t+B1MzQ0hLe3N4YNG8ZZJr64d+9em4U+ZWXlNgf6kLBnzx6EhISgoKAAtra2CA8Ph62traDfsZaWFoKDg4n9u0tPT0dISAhOnToFcXFxzJkzB3v37hW6vo2NDYYPH04kDwBs2LABc+bMwdOnT9HU1ITo6GgUFBQgPDwcly5dIpajhaurK7KysjBixAisWbMGEyZMwIEDB9DQ0IA9e/YQzwM098BuuWnTrVs3lJaWwtDQEAoKCigtLSWSoXPnznjw4AG6du0KJSWlz95EevnyJZFMrY0dOxapqanQ1tYmfu22GBkZcToU62Msy6K0tBQqKirEB1B9ioWFheBnZWVlXL58mcM01H8BLZpSFEVRFEXxUFVVFRQUFETW5eXlOflSFRUVhVmzZsHCwgJDhgwBANy5cwcmJib4+eefMW3aNOKZ+Pblac+ePVi3bh2WLFkCKysrsCyLW7duYeHChXjx4gU8PT2JZzpx4gRcXV1hb28PDw8PsCyL5ORkjB49GqGhoXBwcCCeiU8UFRVRVlYGLS0tofWMjAz07NmTk0yBgYGCafXdu3dv8znq6uo4duwYkTwDBgzAN998g8DAQEyZMqXNY9RGRkaYOXMmkTwAMGnSJJw5cwa+vr5gGAbr16+Hubk5Ll68iG+++YZYjhat/21bW1sjPz8fqamp0NHR4exINR924u/du1ewg3vv3r2823k/YcIEeHt7Izc3FyYmJiLvbdKD8nbs2IGVK1fC19e3zTzy8vJE87Asi969e+P+/fvo3bs30WtTFF/QnqYURVEURVE8ZGxsjIULF2LJkiVC6wcOHEBgYCByc3OJ5tHW1sbs2bPh4+MjtL5hwwZERESguLiYaB4+0tLSwqZNm0QmLoeFhWHjxo0oKSkhnsnQ0BDz588XKdju2bMHR44cQV5eHrEsDQ0NOHnyJMaOHfvJYiBpK1euxO3bt3H27Fno6ekhPT0dFRUVcHJygpOTEzZs2MB1RM49fvxY5Bg8lxoaGrB161a4ublBTU2N6zi8lZqair/++gvW1taorKyEs7MzkpKSBDvxaX9MCHZvt4VhGDQ2NhJM8z95Pi4usyzLSR6guRXGsWPHMHjwYOLXpig+oEVTiqIoiqIoHjp+/DiWLFkCb29vjBo1CgAQFxcHPz8/+Pv7Y968eUTzyMjIIDs7G7q6ukLrhYWFMDU1pUOX0NybLycnp82/IxMTE9TV1RHP1LFjR9y/f18kU1FREYyNjYlnkpGRQV5eHm+KcPX19XBxccHp06fBsiw6dOiAxsZGODg4IDQ0lLMhZ9TnycrKIicnB5qamlxHob7QsWPH4O7uLrLe0NCAdevWYdu2bRyk4pfExMTPPj5ixAhCSf7Hb7/9hu3btyMwMBDGxsbEr09RXKPH8ymKoiiKonjIzc0N79+/x9atW7F582YAgKamJgIDA0V2MpIwcuRI3Lx5U6T4lpSURHtj/v90dXURGRmJH3/8UWj9zJkznB1tVFNTQ1xcnMjrFhcXx8kuvUGDBiEzM5MXRVOWZfHs2TMcOXIEmzdvRnp6OpqammBmZkaPovLcmDFjcOPGDbi4uHAdhfpCXl5euHz5Mo4cOYLOnTsDAPLz8+Hg4IDXr1/Toim4KYr+ndmzZ6O2thampqaQlJQU6W3KRS9aiiKJFk0piqIoiqJ4atGiRVi0aBEqKyshLS0NWVlZotdvPYjKzs4Oq1atQlpamuCY3p07d3D27Fls2rSJaC6+2rRpE2bMmIHff/8dVlZWYBgGSUlJiIuLQ2RkJCeZvLy84OHhgczMTFhaWgoyhYaGcjIdfvHixVi+fDmePHmC/v37o1OnTkKP9+3bl1iWj/v18WUYDPX3xo8fjzVr1iAnJ6fN9xHpXpR80nIy4e/Ex8e3cxJhGRkZmDNnDkxMTBAaGooHDx7A29sb3333HQ4ePEgsx/79+zF//nxISUlh//79n32uh4dHu+fJzs6GsbExxMTEkJ2d/dnnkvx8bMHF0EmK4hN6PJ+iKIqiKIpq0+f6vbXGVa81PkpLS8PevXuRl5cHlmVhZGQELy8vmJmZcZbp/Pnz8PPzE/QvNTQ0hLe3NyZPnkw8S1vvKYZhOOvZx5d+fRcuXMD48ePbHLJEieJbL0o+ERMTg4aGBiZMmPDZ99PevXsJpmrW1NQET09PBAQEQFxcHOHh4UQHiAHNvadTU1PRpUsXkQFwrTEMQ6RXt5iYGMrLy6GiogIxMTHB52Fbef7L7+vW4uLiEBcXh+fPn6OpqUnosePHj3OUivpa0aIpRVEURVEUT0VFRSEyMhKlpaX48OGD0GPp6ekcpeKPT+0SYhgGUlJS0NXVxfDhw2lfSh55/PjxZx8nfWyfL/36xMXFUV5eDmVlZYiLi6OsrAwqKiqc5aH+d968edPmOsMw6NixIyQlJYll2blzJ0JDQ1FVVQVHR0e4ubnxphflhQsXMHfuXOjr66OgoAAmJiaIiIhAjx49uI7GmcePH0NdXR0Mw/Du8xEASktLP/u4uro6oSTNNm3aBB8fH1hYWEBVVVVkaNb58+eJ5qG+frRoSlEURVEUxUP79+/H2rVr4ezsjCNHjsDV1RUPHz7E3bt38f3332Pr1q2cZaurq4OUlBRn12+hpaWFyspK1NbWQklJCSzLorq6GjIyMpCVlcXz58+hra2NhIQEIv07R44cCTc3N0ybNk2k7xtXtLW1cffuXXTp0kVovbq6Gubm5kR2UvGZkpISamtr0dDQwGm/vu7du+PIkSOYNGkSxMTEUFFRAWVlZSLX/juNjY0IDQ395M4u0se8+ahlh+Cn9OrVCy4uLtiwYcMX7+D/f3X79m0cP34ckZGR0NfXh5ubGxwcHCAvL0/k+h9bsGABwsLCsGXLFnh5eaGiogJubm74448/EBgYiOnTpxPP5OPjgxUrVkBGRkZo/d27d9i1axfWr19PNM/vv/8OS0tLdOgg3EWxoaEBycnJGD58ONE8wN+/t0nvflVVVcXOnTsxZ84cotel/rto0ZSiKIqiKIqHDAwMsGHDBsyaNQtycnLIysqCtrY21q9fj5cvXyIgIIBonsbGRvj6+iIoKAgVFRV48OABtLW1sW7dOmhqarY5Fbm9nTp1CocPH8bRo0eho6MDoHkq/IIFCzB//nxYWVlh5syZ6N69O6Kioto9j5eXF06ePIl3795h+vTpcHd35/zYd+ujn61VVFRAXV0d79+/J54pIiICQUFBKCkpwe3bt6GhoQF/f39oaWkRbxkQFhb22cednZ2J5Ni4cSN8fHw+W5xoQbpIsWTJEoSGhmLChAlt7uzi4pg3wK8juuHh4Vi7di1cXFwwcOBAsCyLu3fvIiwsDD/99BMqKyuxe/dueHt7iwyKa2+1tbU4e/YsDh48iNzcXDx79oyTwqmxsTFOnjwJU1NTofWDBw9i1apVePv2LfFMn9rVXVVVBRUVFeL/1viWBwCysrKE/lxfX4+MjAzs2bMHW7duhb29PdE8Xbp0QUpKiuB3PkW1N1o0pSiKoiiK4iEZGRnk5eVBQ0MDKioquH79OkxNTVFYWIjBgwejqqqKaB4fHx+EhYXBx8cH8+bNQ05ODrS1tREZGYm9e/fi9u3bRPMAgI6ODs6dO4d+/foJrWdkZODbb79FcXExkpOT8e2336KsrIxIpsbGRly6dAkhISG4fPkydHV14ebmhjlz5qBbt25EMgD/M8RrypQpCAsLg4KCglDGuLg4XL9+HQUFBcQyAUBgYCDWr1+PH374AVu3bhW8j0JDQxEWFoaEhASiefgkPz8fRUVFsLOzQ0hICBQVFdt8HunCcteuXREeHg5bW1ui1/0cvh3RHT16NBYsWCCyWzIyMhLBwcGIi4tDREQEtm7divz8fKLZkpKScPz4cZw9exZ9+vRBQkICJzvh379/j44dO7b5WEFBAfT19Qknwid3dcfHx2PGjBmorKzkRZ4HDx7AwsLik20guPDbb79h165duHHjBtHrrlq1CrKysli3bh3R61L/XR3+/ikURVEURVEUad27d0dVVRU0NDSgoaGBO3fuwNTUFCUlJW0OiWhv4eHhOHz4MEaPHo2FCxcK1vv27Uu8CNCirKwMDQ0NIusNDQ0oLy8HAPTo0QN//fUXsUzi4uKYPHkyJk+ejMrKSgQHB2PdunX48ccfYWtrCw8Pjy+ebv3/YsqUKQCaeyp+vFtSQkICmpqa8PPza/ccHztw4ACOHDmCKVOmYPv27YJ1CwsLrFixgngeoLmIfP78eeTl5YFhGBgaGmLy5MkiR2Tbm4GBgWCH+bRp00SODHNFUlISurq6XMcQEhQUhNDQUN4c0b19+zaCgoJE1s3MzAQ3lIYOHfq3/SH/Kc+ePUNoaChCQ0Px5s0bzJ49G3/88QeMjIyIXL8tHTt2xMOHDxESEoKHDx9i3759UFFRwdWrV4m0T2lNSUkJDMOAYRjo6ekJFd0bGxvx9u1bod9z7a1ltybDMHBxcREqLjc2NiI7OxuWlpbE8nwJPT093L17l/h16+rqcPjwYcTGxqJv374iw8727NlDPBP1daNFU4qiKIqiKB4aNWoULl68CHNzc7i7u8PT0xNRUVFITU0lfhwOAJ4+fdpm4aSpqQn19fXE8wCAtbU1FixYgKNHjwqm02dkZGDRokWCwuS9e/c+OyG5vaSkpCAkJASnTp2CiooKXFxcUFZWhkmTJmHRokXYvXt3u16/5biylpYW7t69i65du7br9b5USUmJ4LVqrWPHjqipqSGeJycnB5MnT0Z5eblgp9uDBw+grKyMCxcuwMTEhHimDRs2AAAqKytRUFAgKOxw1ePUy8sL+/btQ0BAwBe1DyDhw4cPvCoi9erVC8eOHRO6EQAAx44dExQEq6qqoKSk1O5ZbG1tkZCQABsbG+zatQsTJkwgfgOgLYmJiRg/fjysrKzw+++/Y+vWrVBRUUF2djaOHj1KpIVKC39/f7AsCzc3N2zatEloJ76kpCQ0NTUxZMgQYnlars+yLOTk5IR2AktKSmLw4MGYN28esTytfby7lWVZlJWVYePGjejduzfxPNnZ2YLTJTk5OUKP8eXzifq60OP5FEVRFEVRPNTU1ISmpibBl93IyEgkJSVBV1cXCxcuJDqNGWjeCfjDDz9g9uzZQj1WN23ahNjYWNy8eZNoHgAoLy/HnDlzEBcXJ9ht0tDQgNGjRyMiIgLdunVDQkIC6uvrYWNj0+55nj9/joiICISEhKCwsBCTJk3C3LlzMXbsWMGXudjYWEyZMoWT/n18YGRkhG3btmHy5MlC76P9+/cjLCwMaWlpRPMMHjwYKioqCAsLExS0Xr16BRcXFzx//pyTthO1tbVYsmQJIiIiBD0MxcXF4eTkhAMHDhDfgTp16lQkJCSgc+fO6NOnj8jOrujoaKJ5AP4d0b1w4QKmTZsGAwMDDBgwAAzD4O7du8jPz0dUVBQmTpyIwMBAFBYWtvtOODExMaiqqkJFReWzRaT09PR2zfGxIUOGYNq0aVi+fLnQv/27d+9iypQpePr0KdE8QHMh18rKihdFZaC57cSKFSvQqVMnrqMItDUIimVZqKmp4fTp00SLyxTFBX58OlAURVEURVFCxMTEhKYsT58+nZPpwi02bNiAOXPm4OnTp2hqakJ0dDQKCgoQHh6OS5cucZKpe/fuuH79OvLz8/HgwQOwLAsDAwOh3njW1tbE8vTq1Qs6Ojpwc3ODi4tLmzsDBw4ciAEDBrRrjv3792P+/PmQkpLC/v37P/tcDw+Pds3yMW9vb3z//feoq6sDy7JISUnBqVOnsG3bNhw9epRoFqB5yElqaqrQDkAlJSVs3bq13V+nT/H09ERiYiIuXLgAKysrAM19KT08PODl5YXAwECieRQVFTF16lSi12zL8uXLBT83NTXx6oiunZ0dCgoKEBQUJPgsGj9+PH755RdoamoCABYtWkQkS8tOZb65d+8efv75Z5F1ZWVl4j26W8jJySEvL0+wo/zXX39FSEgIjIyMsHHjRuI3J1euXCnUfufx48c4f/48jIyMiNz4a8vHfabFxMSgrKwMXV1dzovNf/75JxiGQc+ePTnNQX3d6E5TiqIoiqIoHrp69SpkZWUxdOhQAM0Tho8cOQIjIyMcPHiQyDHPj8XExMDX1xdpaWloamqCubk51q9fz9mXOb65efMmhg0bxnUMaGlpITU1FV26dPlsawKGYVBcXEwwWbMjR45gy5YtePLkCQCgZ8+e2LhxI9zd3Yln6devH/bs2SPSZzY+Ph7Lli3DvXv3iGfq2rUroqKiMHLkSKH1hIQETJ8+nfhwGr740hsgDMMgPj6+ndNQ/1u9evVCZGQkLC0thXaanj9/HitWrMDDhw+JZxowYABWr14tGBxoZGQEe3t73L17FxMmTIC/vz/RPDY2NrC3t8fChQtRXV0NfX19SEpK4sWLF9izZw+xwjufNTU1YcuWLfDz8xOc2JCTk4OXlxfWrl0rdLOZov4JtGhKURRFURTFQyYmJtixYwdsbW1x7949WFhYwMvLC/Hx8TA0NERISAjXETnX2NiI0NBQxMXF4fnz54I+ni1o4YTfXrx4gaamJqioqHCW4fLly1i5ciU2btyIwYMHAwDu3LkDHx8fbN++XXDTAgDk5eWJZJKRkUFaWhoMDQ2F1u/fv4+BAwdy0vsV4E+PVb6qrq5GSkpKm59FTk5OHKXij5UrV+L27ds4e/Ys9PT0kJ6ejoqKCjg5OcHJyYmTHbIKCgpIT0+Hjo4OduzYgfj4eMTExODWrVuYOXOm4MYOKV27dkViYiL69OmDo0eP4sCBA8jIyMC5c+ewfv165OXlEc3T4uHDh/D39xcalrds2TLo6OgQz7JmzRocO3YMmzZtgpWVFViWxa1bt7Bx40bMmzcPW7duJZ6J+rrRoilFURRFURQPycrKIicnB5qamti4cSNycnIQFRWF9PR02NraCqbD/5ctWbIEoaGhmDBhAlRVVUX6ru3du5ejZNS/RetdSS3vn5avR63/zDCMoL9oexs9ejS6dOmC8PBwSElJAQDevXsHZ2dnvHz5ErGxsURytKipqcHSpUsRHh4uKAZy1WNVXFwcZWVlnBba23Lx4kU4OjqipqYGcnJyQp9FDMPg5cuXHKbjh/r6eri4uOD06dNgWRYdOnRAY2MjHBwcEBoaCnFxceKZ5OXlkZaWht69e+Obb77BxIkTsWzZMpSWlkJfXx/v3r0jmkdGRgb5+flQV1fH9OnT0adPH2zYsAFPnjyBvr4+amtrieYBmk+Y2NnZoV+/foIiZXJyMrKysnDx4kV88803RPP06NEDQUFBsLOzE1r/9ddfsXjxYk5641JfN9rTlKIoiqIoiockJSUFX5BiY2MFO5U6d+4sMs22vSgpKX3xNFouigKnT59GZGQkbG1tiV/734IPu3HNzMy++H1EejjNx/36+GDfvn0YN24cevXqBVNTUzAMg8zMTEhJSSEmJoZ4nuXLlyMxMREXL17kvMcqX/f7eHl5wc3NDb6+vsQHdf1bSEhI4OTJk9i8eTPS09PR1NQEMzMzTiawt7CwsMCWLVswZswYJCYmCt7LJSUl6NatG/E8urq6+OWXXzB16lTExMTA09MTQPOQQVI73T+2evVqeHp6Yvv27SLrq1atIl40ffnyJQwMDETWDQwM6M0Jql3QoilFURRFURQPDR06FMuXL4eVlRVSUlJw5swZAMCDBw/Qq1cvIhla93OrqqrCli1bMHbsWMG03Nu3byMmJoazCdaSkpLQ1dXl5Nr/FsuWLRPsxjU2Nv7i4uU/acqUKYKf6+rqcOjQIRgZGQneR3fu3MH9+/exePFi4tlGjBhB/Jp/x9jYGIWFhThx4gTy8/PBsixmzpwJR0dHSEtLE89z7tw5kR6rtra2kJaWxvTp04kPpuKjp0+fwsPDgxZMv4C2tja0tbW5jgGg+Xeco6MjfvnlF6xdu1bw+yQqKgqWlpbE86xfvx4ODg7w9PTE6NGjBZ+R165dg5mZGfE8AJCXl4fIyEiRdTc3N+I9XwHA1NQUAQEBIkMOAwICYGpqSjwP9fWjx/MpiqIoiqJ4qLS0FIsXL8aTJ0/g4eEhGJLj6emJxsbGv52K/k/79ttvYW1tjSVLlgitBwQEIDY2Fr/88gvRPADg5+eH4uJiBAQEcFIM/Dfo2rUrwsPDebMbd+7cuVBVVcXmzZuF1luOoB4/fpx4pps3byI4OBjFxcU4e/YsevbsiYiICGhpaQn1NP2v4lOPVTExMYSFhUFBQeGzz/v46G57s7e3x8yZMzF9+nSi1/3fqq6uhqKiItcxeK+urg7i4uKQkJAgfu3y8nKUlZXB1NRU0D4kJSUF8vLybe6wbG9qamrYs2cPpk2bJrQeGRmJFStWoLS0lGiexMRETJgwAerq6hgyZAgYhkFycjKePHmCy5cv82IYI/V1oUVTiqIoiqIo6m/JysoiMzNTZGdnYWEhzMzMBFNsSZo6dSoSEhLQuXNn9OnTR+QLbnR0NNE8nypkMwwDKSkp6OrqYvjw4UR79/Xo0QM3btyAnp4esWt+joKCAlJTU0WO5BYWFsLCwgKvX78mmufcuXOYM2cOHB0dERERgdzcXGhra+PQoUO4dOkSLl++TDQPH/Gpx+qXTMYm2X+2xbFjx+Dj4wNXV1eYmJiIfBaRLuICwI4dO6CpqYkZM2YAAKZPn45z586he/fuuHz5Mt2VR30RHx8f7N27F6tXr4alpSUYhkFSUhJ27NgBLy8v/PTTT8QzPXv2DAcPHhTsxDcyMsLixYvRo0cP4lmorx8tmlIURVEURfFUU1MTioqK2uxFOXz4cKJZNDQ0sGTJEnh7ewut79q1CwEBAXj8+DHRPADg6ur62cdDQkIIJWmmpaWFyspK1NbWQklJCSzLorq6GjIyMpCVlcXz58+hra2NhIQEqKmpEcnEt9243bt3x7Zt20Reu5CQEKxevRoVFRVE85iZmcHT0xNOTk6Qk5NDVlYWtLW1kZmZiXHjxtGBawBycnIwbtw41NXVtdljtU+fPsSyiImJoby8nHeDoD5XzOWiiAs0H4M/ceIELC0tcf36dUyfPh1nzpxBZGQkSktLce3aNeKZ+KBz58548OABunbt+rd9u0n0yLS3t0doaCjk5eVhb2//2eeSvhEINPcR9vf3h5+fH549ewag+Wact7c3PDw8ePF7haLaE+1pSlEURVEUxUN37tyBg4MDHj9+LDL8hIsv4Zs2bYK7uztu3Lgh1Ivy6tWrOHr0KNEsLUgXRf+Or68vDh8+jKNHj0JHRwcAUFRUhAULFmD+/PmwsrLCzJkz4enpiaioKCKZkpKSkJCQgCtXrvBiN+4PP/yARYsWIS0tDYMHDwbQ/D46fvw41q9fTzQLABQUFLR5A0JeXh7V1dXE8/ARn3qs8rVA8/FNLT4oKysT3Jy5dOkSpk+fDhsbG2hqamLQoEEcp+PO3r17IScnJ/iZ6/eUgoKCIMPftZ3gAsMw8PT0hKenJ/766y8AEPz9kZKdnQ1jY2OIiYkhOzv7s8/t27cvoVTUfwXdaUpRFEVRFMVD/fr1g56eHjZt2gRVVVWRL3ZcfLn6448/sH//fuTl5QmOxHl4ePynv4C3pqOjg3PnzqFfv35C6xkZGfj2229RXFyM5ORkfPvttygrKyOSiW+7cYHmXnj79u1DXl4eAMDQ0BDLli3jpB+kjo4OgoODMWbMGKGdpuHh4di+fTtyc3OJZ6I+ja87TfmoR48egoFG+vr62LJlC6ZNm4aCggIMGDAAb968IZ6puroaKSkpbZ6ecHJyIp6H+nsbN26Eq6srNDQ0OMvQ+t+9mJgYGIYRuZkMcLerm/q60Z2mFEVRFEVRPFRYWIioqCheTYcfNGgQTp48yWkGc3NzxMXFQUlJCWZmZp/dJZSenk4wWfPOroaGBpH1hoYGwTHvHj16CHbrkMC33bhAc29FvgzMWbBgAZYtW4bjx4+DYRg8e/YMt2/fxooVKzjZ+dqiuroaUVFRePjwIby9vdG5c2ekp6ejW7du6NmzJ2e5uObs7Ex8d+un7N+/H/Pnz4eUlNTfDubz8PAglOp/2Nvbw8HBAb1790ZVVRXGjx8PAG32pibh4sWLcHR0RE1NDeTk5IQ+uxmG4aRoeuzYMcGQxdYaGhqwbt06bNu2jWie+/fvf7LdxdWrVzFu3DiieYDm123Lli0YMWIE3N3dYW9vL+htTEpJSQmUlZUFP1MUSXSnKUVRFEVRFA+NGjUKK1eu5ORLEp9t2rQJ3t7ekJGRwaZNmz773A0bNhBK1WzChAkoLy/H0aNHYWZmBqB5l+m8efPQvXt3XLp0CRcvXsSPP/6Ie/fuEc1WWVmJgoICMAwDPT09wRdQrnz48KHN3Wbq6urEs6xduxZ79+5FXV0dAKBjx45YsWIFNm/eTDwL0HwUdcyYMVBQUMCjR49QUFAAbW1trFu3Do8fP0Z4eDgnuShhWlpaSE1NRZcuXaClpfXJ5zEMg+LiYoLJmtXX12Pfvn148uQJXFxcBJ9J/v7+kJWVxdy5c4nm0dPTg62tLXx9fSEjI0P02p+iqKiI0aNH48iRI+jcuTMAID8/Hw4ODnj9+jUePnxINI+0tDR27tyJpUuXCtbev38PLy8vHDt2DO/evSOap0V2djZCQkLw888/48OHD5g5cybc3NwwYMAA4ll+//13WFpaokMH4f1/DQ0NSE5OJt7vnfr60aIpRVEURVEUD50/fx4//fQTvL2925zGTPt28U95eTnmzJmDuLg4wevV0NCA0aNHIyIiAt26dUNCQgLq6+thY2NDJFNNTQ2WLl2K8PBwQYFSXFwcTk5OOHDgAPHiRWFhIdzc3JCcnCy0zrIs8aOVjY2NSEpKgomJCaSkpJCbm4umpiYYGRlBVlaWWI6PjRkzBubm5ti5c6dQy4Dk5GQ4ODjg0aNHnGWjqP+rTp064d69e9DW1uY6ikBJSQnmzJmDkpIShIaG4sGDB/D29sZ3332HgwcPEu/dGR0djfnz52PAgAEICQlBeXk5HBwcAAAnTpyAubk50Twfa2howMWLFxESEoKrV69CX18fc+fOhYuLC7GWQeLi4igrKxNp0VFVVQUVFRV6PJ/6x9GiKUVRFEVRFA+1NY25pY8X7dvV7MmTJ2AYBr169QIApKSk4Oeff4aRkRHmz5/PWa78/Hw8ePAALMvCwMAA+vr6nGVZsGABYmNjERAQACsrKwDNw6E8PDzwzTffIDAwkGgeKysrdOjQAatXr26zV6+pqSnRPFJSUsjLy/vsTkHSFBQUkJ6eDh0dHaGi6ePHj6Gvry/YEUtRH7tw4cIXP9fOzq4dk4iyt7fHzJkzedOao0VTUxM8PT0REBAAcXFxhIeHY+bMmZzlefbsGZydnZGRkYGamhq4urrCz8+PF20pPnz4gPPnz+P48eOIj4+HpaUlKioq8OzZMxw5cgQzZsxo9wxiYmKoqKgQOS3x4MEDWFhYcNKrl/q60Z6mFEVRFEVRPET7dv09BwcHzJ8/H3PmzEF5eTnGjBkDY2NjnDhxAuXl5Zz1pDQwMICBgQEn1/7YuXPnEBUVhZEjRwrWbG1tIS0tjenTpxMvmmZmZiItLY03fz8mJiYoLi7mVdFUSkqqzS/+BQUFnLZV4FNLBb5Zvnx5m+sMw0BKSgq6urqYPHmy4Ah4e5kyZYrI9VvvkWp9k4L0jbcJEybA29sbubm5bZ6eIF3EbXHp0iWcOnUKlpaWKCgowJEjRzB8+HD06NGDkzyNjY348OEDGhsb0djYiO7du6Njx46cZGmRlpaGkJAQnDp1Ch07doSTkxMOHjwo6I3r5+cHDw+Pdi2a2tvbA2h+D7u4uAj9nTQ2NiI7OxuWlpbtdn3qv4sWTSmKoiiKoniIy0m1bXFzc8O+fftEjiu2HP8+fvw48Uw5OTkYOHAggOaJ7CYmJrh16xauXbuGhQsXEi+aNjY2IjQ0FHFxcW0Wl+Lj44nmAYDa2lp069ZNZF1FRQW1tbXE8xgZGeHFixfEr/spW7duFfQv7d+/Pzp16iT0uLy8PPFMkydPho+PDyIjIwE0FwlKS0uxevVqfPvtt8Tz8KmlAl9lZGQgPT0djY2N0NfXB8uyKCwshLi4OAwMDHDo0CF4eXkhKSkJRkZG7Zaj9WdObGwsVq1aBV9fXwwZMgQMwyA5ORk//fQTfH192y3Dp8ybNw8A4OPjI/IYV++jBQsWICwsDFu2bIGXlxcqKirg5uYGExMTBAYGEt8Ve/r0aSxatAjDhg3DgwcPkJmZCVdXV8TExCAiIoKT1gZ9+/ZFXl4ebGxscOzYMUyaNAni4uJCz3FycoK3t3e75mg5/s+yLOTk5IR23kpKSmLw4MGC9xhF/ZPo8XyKoiiKoigey83NRWlpKT58+CC0TnpXzqf6iL148QLdu3dvc2p8e5OVlUVOTg40NTVhZ2cHKysrrFq1CqWlpdDX1yc+NGPJkiUIDQ3FhAkT2jx6vnfvXqJ5AGD06NHo0qULwsPDBROP3717B2dnZ7x8+RKxsbFE88THxwuKNm3tNiNdpGzdBqP168VlQfDNmzewtbXF/fv38ddff6FHjx4oLy/HkCFDcPnyZZHCbnvjS0uFlp1mXyI6Orodk4jy9/fHzZs3ERISIngPv3nzBu7u7hg6dCjmzZsHBwcHvHv3DjExMUQyGRsbIygoCEOHDhVav3nzJubPn4+8vDwiOfjM2NgYJ0+eFHkPHzx4EKtWrcLbt2+J5unUqRN2796NRYsWCdZevXqFBQsW4OrVq5wcPd+8eTPc3NzQs2dP4tduy6ZNm7BixQrin4PUfxctmlIURVEURfFQcXExpk6dinv37gkdsWwpWJAq5rx58wYsy0JJSQmFhYVCx4MbGxtx8eJFrF69Gs+ePSOSp7VBgwbB2toaEyZMgI2NDe7cuQNTU1PcuXMH3333Hf7880+iebp27Yrw8HDY2toSve7n5OTkYNy4cairq4OpqSkYhkFmZiakpKQQExODPn36EM3TUqT8uPDGVZEyMTHxs4+PGDGCUBJR8fHxSE9PR1NTE8zNzTFmzBhOcnTq1IkXLRVcXV0FP7Msi/Pnz0NBQQEWFhYAmo8QV1dXw97eHiEhIUSz9ezZE9evXxfZRXr//n3Y2Njg6dOnSE9Ph42NDbGd1tLS0khJSYGJiYnQenZ2NgYNGsTZJHY+ef/+/SePvhcUFBDvR/25a0ZERGDOnDlE81AURY/nUxRFURRF8dKyZcugpaWF2NhYaGtrIyUlBVVVVfDy8sLu3buJ5VBUVATDMGAYBnp6eiKPMwyDTZs2EcvT2o4dOzB16lTs2rULzs7Ogt1CFy5cEBzbJ0lSUlLQ440vjI2NUVhYiBMnTiA/Px8sy2LmzJlwdHTkZLBIQkIC8Wt+DpdF0b8zatQojBo1CgBQXV3NWQ6+tFRoXQhdtWoVpk+fjqCgIMFR4cbGRixevJiTlgqvX7/G8+fPRYqmlZWVgt2BioqKIicG2tOAAQPwww8/4MSJE1BVVQUAlJeXw8vLi5PPR6C5nUtiYmKbpyc8PDyI5+nYsSMePnyIkJAQPHz4EPv27YOKigquXr0KNTU14nn09fXR0NCAGzdu4OHDh3BwcICcnByePXuGqVOnEs/DV1FRUYiMjGzzfZSens5RKuqrxVIURVEURVG806VLFzYrK4tlWZaVl5dn8/PzWZZl2bi4OLZfv37Ecty4cYNNSEhgGYZho6Oj2Rs3bgj+l5yczD59+pRYlrY0NDSwL1++FForKSlhKyoqiGfZvXs3u3jxYrapqYn4tan/mytXrrA3b94U/DkgIIA1NTVlZ82aJfK+ImX79u3s6dOnBX+eNm0aKyYmxvbo0YPNzMwknicuLo4dMmQIm5CQwL548YJ9/fq10P+40LVrV8FnYmv5+fls586diedxcHBgtbS02OjoaPbJkyfsn3/+yUZHR7Pa2trs7NmzWZZl2VOnTrH9+/cnlqmwsJA1NjZmJSQkWB0dHVZHR4eVkJBg+/TpwxYWFhLL0SI9PZ3t3r07Ky8vz4qLi7PKysoswzBsp06dWC0tLeJ5WLb595u0tDQ7ZswYVlJSkn348CHLsiy7Y8cO9ttvvyWe59GjR6yBgQErIyPDiouLC/IsW7aMXbBgAfE8fLRv3z5WVlaW/f7771lJSUl2wYIF7JgxY1gFBQX2xx9/5Doe9RWix/MpiqIoiqJ4SElJCWlpadDW1oaOjg6OHj0Ka2trPHz4ECYmJsSH+Dx+/Bjq6uoix6r5oLKyEgUFBYLdsFxNGJ86dSoSEhLQuXNn9OnTR6RfJ8k+i7///vsXPW/48OHtnETUzZs3ERwcjOLiYpw9exY9e/ZEREQEtLS0RPovtjcTExPs2LEDtra2uHfvHiwsLODl5YX4+HgYGhoSP+YNANra2jhx4gQsLS1x/fp1TJ8+HWfOnBHsrLp27RrRPHxrqQA0fz6GhISITIv/5Zdf4OrqilevXhHN8/btW3h6eiI8PFzQ37lDhw5wdnbG3r170alTJ2RmZgIA+vXrRywXy7K4fv26YJe5kZERxowZw8nn+MiRI6Gnp4fAwEAoKioiKysLEhISmD17NpYtW/a/6ln7TxkyZAimTZuG5cuXQ05ODllZWdDW1sbdu3cxZcoUPH36lGieKVOmQE5ODseOHUOXLl0EeRITEzF37lwUFhYSzcNHBgYG2LBhA2bNmiX0mq1fvx4vX75EQEAA1xGprww9nk9RFEVRFMVDxsbGyM7Ohra2NgYNGoSdO3dCUlIShw8f5mSCbnx8PGRlZTFt2jSh9bNnz6K2thbOzs7EM9XU1GDp0qUIDw8XTI0WFxeHk5MTDhw4ABkZGaJ5FBUVeXOEcuTIkYLCyKf2SHBR8Dp37hzmzJkDR0dHpKen4/379wCAv/76C76+vrh8+TLRPCUlJYIj1efOncOkSZPg6+uL9PR0znrTlpWVCY4GX7p0CdOnT4eNjQ00NTUxaNAg4nn41lIBaO5v6ubmhqKiIgwePBgAcOfOHWzfvl2o9ykpsrKyOHLkCPbu3Yvi4mKwLAsdHR3IysoKnkOyWNqCYRjY2NjAxsaG+LU/lpmZieDgYIiLi0NcXBzv37+HtrY2du7cCWdnZ06Kpvfu3cPPP/8ssq6srIyqqirieZKSknDr1i1ISkoKrWtoaBAv4PJVaWkpLC0tATT37f3rr78AAHPmzMHgwYNp0ZT6x9GiKUVRFEVRFA/99NNPqKmpAQBs2bIFEydOxLBhw9ClSxecOXOGeJ7t27cjKChIZF1FRQXz58/npGi6fPlyJCYm4uLFi7CysgLQ/KXTw8MDXl5eCAwMJJqHi12Jn6KkpAQ5OTm4uLhgzpw56Nq1K9eRADS/l4OCguDk5ITTp08L1i0tLeHj40M8j6SkpGDXdmxsLJycnAAAnTt35mRSNdD82j158gRqamq4evUqtmzZAqC5+M3Frk4+9n3dvXs3unfvjr1796KsrAwAoKqqipUrV8LLy4uzXLKysujbty9n1/9YXFwc4uLi8Pz5c8GNpRbHjx8nmkVCQkJwI6dbt24oLS2FoaEhFBQUUFpaSjRLC0VFRZSVlUFLS0toPSMjg5Np8U1NTW3+G//zzz8hJydHPE+L6upqpKSktPk+avnMJKV79+6oqqqChoYGNDQ0BAMgS0pKPnmDkKL+X9CiKUVRFEVRFA+NHTtW8LO2tjZyc3Px8uVLKCkpcXK08vHjxyJfLIHmHTBcfeE9d+4coqKiMHLkSMGara0tpKWlMX36dOJFUz4pKyvD+fPncfz4cezcuRO2trZwd3fHuHHjOG2xUFBQ0GZLAHl5eU6GHQ0dOhTLly+HlZUVUlJSBDckHjx4gF69ehHPAwD29vZwcHBA7969UVVVhfHjxwNo3qnH1aCx6upqHDt2DHl5eWAYBkZGRnBzc4OCggInecTExLBy5UqsXLlSUNzmYgBUa3fv3sXZs2fbHE5DsjVHi02bNsHHxwcWFhZQVVXlvLWKmZkZUlNToaenB2tra6xfvx4vXrxAREQETExMOMnk4OCAVatW4ezZs2AYBk1NTbh16xZWrFhBvBgIAN988w38/f1x+PBhAM07hd++fYsNGzZwtvP94sWLcHR0RE1NDeTk5ITeRwzDEP97GjVqFC5evAhzc3O4u7vD09MTUVFRSE1N5WS3MvX1oz1NKYqiKIqieKahoQFSUlLIzMyEsbEx13EAAOrq6ggICICdnZ3Q+q+//orvv/8ef/75J/FMMjIySEtLg6GhodD6/fv3MXDgQMFO3fZkbm6OuLg4KCkpwczM7LOFCa6m+j558gQhISEICwvD+/fv4ezsjE2bNqFDB/L7J3R0dBAcHIwxY8YI9aMLDw/H9u3bkZubSzRPaWkpFi9ejCdPnsDDwwPu7u4AAE9PTzQ2NmL//v1E8wBAfX099u3bhydPnsDFxQVmZmYAAH9/f8jKymLu3LlE86SmpmLs2LGQlpbGwIEDwbIsUlNT8e7dO1y7dg3m5uZE8/DR6dOn4eTkBBsbG1y/fh02NjYoLCxEeXk5pk6dyskudFVVVezcuRNz5swhfu22pKam4q+//oK1tTUqKyvh7OyMpKQk6OrqIiQkBKampsQz1dfXw8XFBadPnwbLsujQoQMaGxvh4OCA0NBQiIuLE83z7NkzWFtbQ1xcHIWFhbCwsEBhYSG6du2K33//HSoqKkTzAICenh5sbW3h6+tLvOVNW5qamtDU1CT4/RUZGSl4Hy1cuFCktQFF/b+iRVOKoiiKoige0tHRQXR0NCdfJNuycuVKREZGIiQkRLBTMDExEW5ubvjuu++we/du4plGjx6NLl26IDw8HFJSUgCAd+/ewdnZGS9fvkRsbGy7Z9i0aRO8vb0hIyODTZs2ffa5GzZsaPc8n1NSUgJ3d3ckJiaisrISnTt3Jp5h586dCAsLw/Hjx/HNN9/g8uXLePz4MTw9PbF+/XosWbKEeCbq84YNGwZdXV0cOXJEUKhoaGjA3LlzUVxc/MVDx/5JFRUVWLFiheDo+cdfaUm3Mejbty8WLFiA77//XnAzQEtLCwsWLICqqurffja0hy5duiAlJQU6OjrEr/1vU1xcjPT0dDQ1NcHMzAy9e/fmLMu7d+9w+vRppKWloampCebm5nB0dIS0tDQneTp16oR79+5x0kudoviAFk0piqIoiqJ4KCQkBGfPnsWJEyc4KW597MOHD5gzZw7Onj0rKJw0NTXByckJQUFBnOzuyMnJwbhx41BXVwdTU1MwDIPMzExISUkhJiYGffr0IZ6Jb96/f49z587h+PHjuH37NiZMmAA3NzeMGzeOs0xr167F3r17UVdXBwDo2LEjVqxYgc2bNxO5/ps3bwRHuf+ubykXR77Dw8M/+zjp47DS0tLIyMiAgYGB0Hpubi4sLCwEPWFJGj9+PEpLS7FkyZI2j55PnjyZaJ5OnTrh/v370NTURNeuXZGQkAATExPk5eVh1KhRgr6rJK1atQqysrJYt24d8WtTXw97e3vMnDkT06dP5zoKgOb/NuLbUErq60aLphRFURRFUTzy+++/Y8iQIRg4cCCKiopQX18PDQ0NdOrUSeh5XB31fvDgAbKysiAtLQ0TExNoaGhwkqPFu3fvcOLECeTn54NlWRgZGXG2K+fJkydgGEbQCzMlJQU///wzjIyMMH/+fKJZUlJSEBISgtOnT0NLSwsuLi6YPXs2LwrwAFBbW4vc3Fw0NTXByMhIaMp4exMXF0dZWRlUVFQgJibWZksFlmXBMAwng5eUlJSE/lxfX4/a2lpISkpCRkYGL1++JJqnW7duiIiIEJnAHhMTAycnJ1RUVBDNAwBycnK4efMmJxPp26KmpobLly/DxMQEpqamWL16NWbNmoXbt29j3LhxeP36NfFMy5YtQ3h4OPr27Yu+fftCQkJC6PE9e/YQz0T9+xw7dgw+Pj5wdXWFiYmJyPvo45Y97U1fXx9BQUGwtrYWWk9MTMT8+fNRUFBANA/19aODoCiKoiiKonjE2toaZWVlmDJlCtdR2qSpqQmWZaGjo8NJT8yPSUtLY968eVzHANA8VGT+/PmYM2cOysvLMWbMGBgbG+PEiRMoLy/H+vXriWUZPHgw1NXV4eHhgf79+wMAkpKSRJ5H+gtvCxkZGVhYWHBy7fj4eEHxOCEhgZMMn/Pq1SuRtcLCQixatAje3t7E88yYMQPu7u7YvXs3LC0twTAMkpKS4O3tjVmzZhHPAzQXKfm092fYsGG4fv06TExMMH36dCxbtgzx8fG4fv06Ro8ezUmm7OxsQVE5JydH6DGuh0JR/x4tv199fHxEHuPixhIfh1JSXze605SiKIqiKIpHxMTEUF5ezsnAh8+pra3F0qVLERYWBqB5x6m2tjY8PDzQo0cPrF69mkiOCxcufPFzSRcElZSUcOfOHejr62P//v04c+YMbt26hWvXrmHhwoUoLi4mlkVMTOxvn0PqC+//ZqIxF1PG/y1SU1Mxe/Zs5OfnE73uhw8f4O3tjaCgIDQ0NAAAJCQksGjRImzfvh0dO3YkmgcArl27Bj8/PwQHB0NTU5P49T/28uVL1NXVoUePHmhqasLu3bsFw2nWrVsnsnuYoqj/Gz4OpaS+btxvD6AoiqIoiqKE8HEX0Jo1a5CVlYUbN24I9cMcM2YMNmzYQKxo+qU7cLnYAVNfXy8oIMXGxgq+1BkYGBDvadjU1ET0ep+joKDAdQQRX7ojSV1dvZ2TfDlxcXE8e/aM+HUlJSWxb98+bNu2DQ8fPgTLstDV1eV0kvaMGTNQW1sLHR0dyMjIiBwZJt3CoHXbCzExMaxcuRIrV64kmoGi/gtmzpwJDw8PyMnJCQ2lXLZsGWbOnMlxOuprRIumFEVRFEVRPLNu3bq/LUiQ7kf3yy+/4MyZMxg8eLBQUdfIyAgPHz4kloNPxcCP9enTB0FBQZgwYQKuX78uGGz07NkzdOnSheN03AkJCeE6gojWxztbDt61fl9z2dP0493ULMuirKwMAQEBsLKyIp6nhYyMDExMTDi7fmv+/v5cRxDR1NSEoqIiPH/+XORzqqW4Q5K1tfVnb8DFx8cTTNMsMTERu3fvRl5eHhiGgaGhIby9vTFs2DDiWVpUV1cjJSWlzdeN9NC1Fh8+fGgzD1c3cfj0um3ZsgWPHz/G6NGjRYZS+vr6Es9Dff1o0ZSiKIqiKIpn7t2799lp9FzsRK2srGyzZUBNTQ0vd8ZyYceOHZg6dSp27doFZ2dnmJqaAmgugg0cOJDjdFRrLQO7XFxcMGnSJF70523x8W5qhmGgrKyMUaNGwc/Pj0gGe3t7hIaGQl5e/m/bK3DRUoFvE7Lv3LkDBwcHPH78WKTXKlfF94+HZNXX1yMzMxM5OTmc/P2dOHECrq6usLe3h4eHB1iWRXJyMkaPHo3Q0FA4ODgQz3Tx4kU4OjqipqYGcnJyQr/LGIYhXjQtLCyEm5sbkpOThda5vInDt9dNUlISZ86cwebNm3k1lJL6etGephRFURRFUTzC156mI0aMwHfffYelS5dCTk4O2dnZ0NLSwpIlS1BUVISrV68SyxIfH48lS5bgzp07kJeXF3rs9evXsLS0RGBgICe7uxobG/HmzRuhHoaPHj2CjIwM717T/7Ly8nKEhYUhNDQUr169wuzZs+Hu7g5DQ0Ouo/GCq6sr9u/fDzk5Obi4uHz2xgjXO4nfvXuH+vp6obWPPxfaW79+/aCnp4dNmzZBVVVV5O+LTy0qNm7ciLdv32L37t1Er2toaIj58+fD09NTaH3Pnj04cuQI8vLyiOYBAD09Pdja2sLX15fTdhMtrKys0KFDB6xevbrN91HLjTiS+Pi6URRJtGhKURRFURTFI+Li4igrK+NdgS05ORnjxo2Do6MjQkNDsWDBAty/fx+3b99GYmKiYEI7CXZ2drC2thb5Etdi//79SEhIwPnz54llaq2yshIFBQVgGAZ6enpQVlbmJAf1ZZKSkhASEoKzZ8/CyMgI7u7ucHd3/6JhWhQ3ampqsGrVKkRGRqKqqkrkcdI78jp16oSsrCzo6uoSve7/RVFREQYOHEi872vHjh1x//59kb+joqIiGBsbo66ujmgeoPl1u3fvHrS1tYlfuy2dOnVCWloaDAwMuI4iwIfXbfny5di8eTM6deqE5cuXf/a5pFsXUV8/+l8CFEVRFEVRPMLX+9mWlpa4deuWYPjKtWvX0K1bN9y+fZtowRQAsrKyhIZRfczGxgZpaWkEEzWrqamBm5sbVFVVMXz4cAwbNgw9evSAu7s7amtrieehvszQoUNx7NgxFBYWQkZGBgsXLkR1dTXXsXhj1KhRbf59vHnzBqNGjSIfCMDKlSsRHx+PQ4cOoWPHjjh69Cg2bdqEHj16IDw8nHieQYMGoaioiPh1/y9u374NKSkp4tdVU1NDXFycyHpcXBzU1NSI5wGAsWPHIjU1lZNrt8XIyAgvXrzgOoYQPrxuGRkZgt3k6enpyMjIaPN/mZmZRPJQ/y38ad5DURRFURRFISQkhFdHOVszMTFBWFgY1zFQUVEhMi27tQ4dOqCyspJgombLly9HYmIiLl68KBjYk5SUBA8PD3h5eSEwMJB4Jj6pr6+HjY0NgoODoaenx3UcgeTkZBw/fhxnz56Fvr4+Dh48CEVFRa5j8caNGzfw4cMHkfW6ujrcvHmTg0TNvSjDw8MxcuRIuLm5YdiwYdDV1YWGhgZOnjwJR0dHonmWLl0KLy8vlJeXw8TEROTzqW/fvkTzABDpRdsyUCw1NRXr1q0jnsfLywseHh7IzMyEpaUlGIZBUlISQkNDsW/fPuJ5AGDChAnw9vZGbm5um6+bnZ0d0Tw7duzAypUr4evr22Ye0m0nAH68bvv27RP8f79x4waRa1JUC3o8n6IoiqIoimrTmzdvvvi5JL/M6ejoYPfu3Zg6dWqbj0dHR2PFihUoLi4mlgkAunbtiqioKIwcOVJoPSEhAdOnTydWyFVSUvri4Vykj+gqKysjOTkZvXv3Jnrdj5WVlSE8PBwhISF49eoVHB0d4e7ujj59+nCai0+ys7MBNPfrjI+PR+fOnQWPNTY24urVqwgODsajR4+IZ5OVlcX9+/ehoaGBXr16ITo6GgMHDkRJSQlMTEzw9u1bonnaauXAMAynA3xcXV2F/iwmJiYYKGZjY0M8DwCcP38efn5+gj6YLVPYJ0+ezEmez7Xg4OJ1a8nz8ec3l+8jgPvXrXXbIm1tbdy9exddunQhcm2KojtNKYqiKIqiqDYpKir+bfGNiy9ztra2WL9+PcaPHy9yzPTdu3fYsGEDJk6cSCxPi9raWnTr1k1kXUVFhejxfH9/f8HPVVVV2LJlC8aOHYshQ4YAaD6eGxMTw8luMycnJxw7dgzbt28nfu3WNDQ00KNHDzg7O8POzg4SEhJobGwUFApbkN4h2NDQgK1bt8LNzY2zI8st+vXrB4ZhwDBMm8fwpaWlceDAAQ6SAdra2nj06BE0NDRgZGSEyMhIDBw4EBcvXuRkl3BJSQnxa35KcXExtLS0OB/Q1ZapU6d+8mYXF5qamriOICQhIYHrCG3i+nVTVFRESUkJVFRU8OjRI969btTXje40pSiKoiiKotqUmJj4xc8dMWJEOyYRVlFRAXNzc4iLi2PJkiXQ19cHwzDIy8vDwYMH0djYiPT09DYLmO1p9OjR6NKlC8LDwwXF3Hfv3sHZ2RkvX75EbGws0TwA8O2338La2hpLliwRWg8ICEBsbCx++eUXonmWLl2K8PBw6OrqwsLCAp06dRJ6nNQQj9Y7zFpuDHz8tYirnV2ysrLIycmBpqYm8Wu39vjxY7AsC21tbaSkpAgNNJOUlISKigrExcU5ybZ3716Ii4vDw8MDCQkJmDBhAhobG9HQ0IA9e/Zg2bJlnOTig4+HCc6YMQP79+8n/nn4sU/tEKyuroa5uTnxkwHUv8f8+fMRHh4OVVVVlJaWolevXp/87KHvI+qfRoumFEVRFEVR1L/O48ePsWjRIsTExAiKXQzDYOzYsTh06BAnBaecnByMGzcOdXV1MDU1BcMwyMzMhJSUFGJiYjg5+i0rK4vMzEyRyceFhYUwMzMjfozZ2tr6k48xDIP4+HgiOR4/fvxFz9PQ0GjnJKKmTJmCKVOmwMXFhfi1/61KS0uRmpoKHR0dmJqacpYjNzcXpaWlIj1gSfbGFBMTQ3l5uaBoKicnh6ysLM4nxH+cq0VFRQXU1dXx/v17TnLV1NQgMTGxzdfNw8ODk0y1tbVt5uGiNy5fXL16FUVFRfDw8ICPjw/k5OTafN5/+YYJ1T7o8XyKoiiKoiiqTR8fVf4c0l/mNDQ0cPnyZbx69QpFRUVgWRa9e/eGkpIS0RytGRsbo7CwECdOnEB+fj5YlsXMmTPh6OgIaWlpTjJ16dIF58+fh7e3t9D6L7/8wklPOL4cP+WiGPqlxo8fjzVr1iAnJwf9+/cX2Y1LejhNCz4UBD9FXV0d6urqnF2/uLgYU6dOxb179wS9TIH/2cXMVS9KPrhw4YLg55iYGKFBh42NjYiLi+NsV3VGRgZsbW1RW1uLmpoadO7cGS9evICMjAxUVFSIF00rKyvh6uqKK1eutPn4f/l9NG7cOABAWloali1b9smiKUX90+hOU4qiKIqiKJ7g2wAfMTExoQLAp3A5oIL6vNDQULi7u2PcuHGCnqZ37tzB1atXcfToUbqbkYf4NpyGFgT/3qRJkyAuLo4jR44I2hlUVVXBy8sLu3fvxrBhw4hlERcXR3l5uaCdgpycHLKzs6GlpUUsQ2uthxt9/LtEQkICmpqa8PPz46QP9ciRI6Gnp4fAwEAoKioiKysLEhISmD17NpYtWwZ7e3uieRwdHfHo0SP4+/vD2toa58+fR0VFBbZs2QI/Pz9MmDCBaB6KouhOU4qiKIqiKN7g2wAfPg034avWu6j+Dhc78lxcXGBoaIj9+/cjOjoaLMvCyMgIt27dwqBBg4jnsba2/uyNAVLH8/mMb0NOli1bBi0tLcTGxrZZEKSaP5vj4+OhrKwMMTExiImJYejQodi2bX7NbQ8AAMJ1SURBVBs8PDyQkZFBLAvLsnBxcUHHjh0BAHV1dVi4cKHIjuXo6GgieVrez1paWrh79y66du1K5LpfIjMzE8HBwRAXF4e4uDjev38PbW1t7Ny5E87OzsSLpvHx8fj1118xYMAAiImJQUNDA9988w3k5eWxbdu2/2zR1N7eHqGhoZCXl//b14TU+5r676BFU4qiKIqiKJ5wdnYW/Pztt9/Cx8dHaICPh4eHYICPp6dnu+fh8xFmvpgyZcoXPY/L3biDBg3CyZMnObn2x/r16yf05/r6emRmZiInJ0fo/U81q6urEwwV4wqfCoJ81djYCFlZWQBA165d8ezZM+jr60NDQwMFBQVEs3z872j27NlEr/8pfLwJJyEhIbiJ061bN5SWlsLQ0BAKCgooLS0lnqempkbQ87Vz586orKyEnp4eTExMkJ6eTjwP0PzeDg0NRVxcHJ4/fy5yU4fEjS4FBQXB6yQvL//FJ3Io6p9Ai6YURVEURVE8FBMTgx07doisjx07FqtXr+YgEfDw4UP4+/sjLy8PDMPA0NAQy5Ytg46ODid5+IBvuwLb0tTUhKKioja/8A4fPpxolr1797a5vnHjRuJDqfiqsbERvr6+CAoKQkVFBR48eABtbW2sW7cOmpqacHd3J56HLwVBvjI2NkZ2dja0tbUxaNAg7Ny5E5KSkjh8+DDxAUwhISFEr/e/ERcX98ni2/Hjx4nnMTMzQ2pqKvT09GBtbY3169fjxYsXiIiIgImJCfE8+vr6KCgogKamJvr164fg4GBoamoiKCgIqqqqxPMAzTvNQ0NDMWHCBBgbG3NSsGz9ng4NDSV+feq/jRZNKYqiKIqieIhvA3xiYmJgZ2eHfv36wcrKCizLIjk5GX369MHFixfxzTffEM9E/b07d+7AwcEBjx8/FuknyKdetLNnz8bAgQPpcW8AW7duRVhYGHbu3Il58+YJ1k1MTLB3717iRVO+FATfvHnzxc+Vl5dvxySifvrpJ9TU1AAAtmzZgokTJ2LYsGHo0qULzpw5QzQLX23atAk+Pj6wsLCAqqoqL3YL+vr64q+//gIAbN68Gc7Ozli0aBF0dXU5KT7/8MMPKCsrAwBs2LABY8eOxcmTJyEpKclZsfD06dOIjIyEra0tJ9f/2KhRoxAdHQ1FRUWh9Tdv3mDKlCm0xQv1j6ODoCiKoiiKoniIbwN8zMzMMHbsWGzfvl1offXq1bh27RpnRwf5ID4+HkuWLMGdO3dEijWvX7+GpaUlAgMDie/qBJqPw+vp6WHTpk1tFipaT7LmUkREBFatWoVnz561+7XMzMy+uGDDxftaV1cXwcHBGD16NOTk5JCVlQVtbW3k5+djyJAhePXqFdE8MTExqKmpgb29PYqLizFx4kTk5+cLCoKjRo0ikqNlMN3nsCzLm5sBL1++/F8N9/vaqaqqYufOnZgzZw7XUf41amtrkZ+fD3V1dc56wfbo0QM3btyAnp4eJ9f/mJiYGMrLywVtDFo8f/4cPXv2RH19PUfJqK8V3WlKURRFURTFQ3wb4JOXl4fIyEiRdTc3N6EBVv9F/v7+mDdvXpu72xQUFLBgwQLs3buXk6JpYWEhoqKioKurS/zabfl4iAfLsigrK0NqaiqxAWet+9DW1dXh0KFDMDIyEro5cf/+fSxevJhIno89ffq0zderqamJk4LA2LFjBT9ra2sjNzeXk4JgQkICsWv9Ezp37sx1BF758OEDLC0tuY7xryIjIwNzc3NOM3h5eWHfvn0ICAjg9AZAdna24Ofc3FyUl5cL/tzY2IirV6+iZ8+eXESjvnK0aEpRFEVRFMVTfBrgo6ysjMzMTPTu3VtoPTMzU2THx39NVlZWm/1nW9jY2HB27HzQoEEoKiriTdH0452tYmJi0NfXh4+PD2xsbIhk2LBhg+DnuXPnwsPDA5s3bxZ5zpMnT4jk+VifPn1w8+ZNkUFsZ8+ehZmZGSeZPsZFQXDEiBHEr/l33Nzcvuh5XPTr5Ju5c+fi559/JnZzhPpnJCUlISEhAVeuXEGfPn0gISEh9DipafX9+vUDwzBgGKbN3e3S0tI4cOAAkSzUfwstmlIURVEURfHUw4cPERISguLiYvj7+0NFRQVXr16Fmpoa+vTpQzTLvHnzMH/+fBQXF8PS0hIMwyApKQk7duyAl5cX0Sx8U1FRIfJFsrUOHTqgsrKSYKL/sXTpUnh5eaG8vBwmJiYiOfv27Us0D9+G1Jw9exapqaki67Nnz4aFhQUnxa4NGzZgzpw5ePr0KZqamhAdHY2CggKEh4fj0qVLxPPU1dXhwIEDSEhIaHOAD5etOWpra1FaWooPHz4IrZN6X4eGhkJDQwNmZmYiPYMpYXV1dTh8+DBiY2PRt29fkc+iPXv2cJSM+hxFRUVMnTqV6xgoKSkBy7LQ1tZGSkoKlJWVBY9JSkpCRUUF4uLiHCakvla0aEpRFEVRFMVDiYmJGD9+PKysrPD7779jy5YtUFFRQXZ2No4ePYqoqCiiedatWwc5OTn4+flhzZo1AJp7nW3cuBEeHh5Es/BNz549ce/evU/u5szOzuZs8vG3334LQHhHHMMwnPd+TEtLQ15eHhiGgZGREWc7KKWlpZGUlCSygzopKQlSUlKcZJo0aRLOnDkDX19fMAyD9evXw9zcnLOBa25ubrh+/Tq+++47DBw4kBc9OisrK+Hq6oorV660+Tip9/XChQtx+vRpFBcXw83NDbNnz+b0WP6FCxe++Ll2dnbtmERUdnY2+vXrBwDIyckReowP7ymqbXy50dWy8/7jmzYU1d7oICiKoiiKoigeGjJkCKZNm4bly5cLDYO5e/cupkyZgqdPn3KWrWXasJycHGcZ+GTp0qW4ceMG7t69K1Joe/fuHQYOHAhra2vs37+feLbHjx9/9vGPj4C3t+fPn2PmzJm4ceMGFBUVwbIsXr9+DWtra5w+fVpo9xAJ27dvx8aNGzF37lwMHjwYQHNP0+PHj2P9+vVYvXo10Tx8pKCggMuXL8PKyorrKAKOjo549OgR/P39YW1tjfPnz6OiogJbtmyBn58fJkyYQCzL+/fvER0djePHjyM5ORkTJkyAu7s7bGxsiBcDxcTEhP7ccoOk9Z9b8GFYFsV/R44cwciRI0VuLHHp4cOH8Pf3F9x4MzQ0xLJly6Cjo8N1NOorRIumFEVRFEVRPCQrK4t79+5BS0tLqGj66NEjGBgYoK6ujmiekpISNDQ0iHxxKiwshISEBDQ1NYnm4ZOKigqYm5tDXFwcS5Ysgb6+PhiGQV5eHg4ePIjGxkakp6ejW7duXEfl3IwZM/Dw4UNERETA0NAQQPNQD2dnZ+jq6uLUqVPEM0VGRmLfvn3Iy8sDAMEX8OnTpxPP0lpqaqpQUaB///6c5DAyMsLp06eJt3L4HFVVVfz6668YOHAg5OXlkZqaCj09PVy4cAE7d+5EUlISJ7keP36M0NBQhIeHo76+Hrm5uZCVleUkS2xsLFatWgVfX18MGTIEDMMgOTkZP/30E3x9fTnZtcxHiYmJ2L17t9C/NW9vbwwbNoyTPNXV1Th27JhQHnd3d5F+0KQYGBjgwYMH6N69O0aMGIGRI0dixIgRMDAw4CRPTEwM7Ozs0K9fP1hZWYFlWSQnJyMrK4uz3fjU140WTSmKoiiKonioV69eiIyMhKWlpVDR9Pz581ixYgUePnxINM+IESPg5uYGZ2dnofUTJ07g6NGjuHHjBtE8fPP48WMsWrQIMTExgp1dDMNg7NixOHToEOdF5dzc3DZ7P5I+oqugoIDY2FgMGDBAaD0lJQU2Njaorq4mmoeP/vzzT8yaNQu3bt2CoqIigOZCiqWlJU6dOgU1NTWiea5cuYL9+/cjKCiI+M7kT5GXl0d2djY0NTWhqamJkydPwsrKCiUlJejTpw9qa2s5yVVaWorQ0FCEhobiw4cPyM/P56xoamxsjKCgIAwdOlRo/ebNm5g/f77gJkF7sre3R2hoKOTl5WFvb//Z55IaKNTaiRMn4OrqCnt7e6EC3Pnz5xEaGgoHBweieVJTUzF27FhIS0tj4MCBYFkWqampePfuHa5duwZzc3OieVqUl5cjISEBiYmJuHHjBgoLC6GsrIyRI0fi9OnTRLOYmZlh7Nix2L59u9D66tWrce3aNU57LFNfJ9rTlKIoiqIoioccHBywatUqnD17FgzDoKmpCbdu3cKKFSvg5OREPE9GRkabx3MHDx6MJUuWEM/DNxoaGrh8+TJevXqFoqIisCyL3r17Q0lJidNcxcXFmDp1Ku7duyd0VLflmC7pI7pNTU1tDs2SkJDgtFfdhw8f2hxypK6uTjyLm5sb6uvrkZeXB319fQBAQUEB3Nzc4O7ujmvXrhHNY2Fhgbq6Omhra0NGRkbk9Xv58iXRPACgr6+PgoICaGpqol+/fggODoampiaCgoKI9w9ufTw/KSkJEydOREBAAMaNGydyXJ6khw8ftrk7UUFBAY8ePSKSQUFBQfBZw9VOyc/ZunUrdu7cCU9PT8HasmXLsGfPHmzevJl40dTT0xN2dnY4cuQIOnRoLtU0NDRg7ty5+OGHH/D7778TzdOie/fumDVrFuzs7JCUlITTp0/jxIkTxHurA0BeXh4iIyNF1t3c3ODv7088D/X1oztNKYqiKIqieKi+vh4uLi44ffo0WJZFhw4d0NjYCAcHB4SGhhKfEqugoIAbN26IDOxJS0vDyJEjBX1OKX6ZNGkSxMXFceTIEcHU4aqqKnh5eWH37t3Ej6BOnjwZ1dXVOHXqFHr06AEAePr0KRwdHaGkpITz588TzVNYWAg3NzckJycLrXM5KEtaWhrJycki/9bS09NhZWWFd+/eEc0zZswYlJaWwt3dHd26dRPp0/nx7nMSTp48KfiMzMjIwNixY1FVVQVJSUmEhoZixowZRHIsXrwYp0+fhrq6OlxdXTF79mx06dKFyLX/zvDhwyEhIYETJ04ICsnl5eWYM2cOPnz4gMTERI4Tcq9jx464f/++yBC/oqIiGBsbE2+DIy0tjYyMDJGj77m5ubCwsOBkB/WVK1cEO0yzsrLQp08fDB8+HCNHjsSwYcOI3xhUU1PDnj17MG3aNKH1yMhIrFixAqWlpUTzUF8/utOUoiiKoiiKhyQkJHDy5En4+PggIyMDTU1NMDMz42wYw7Bhw7Bt2zacOnVKULBtbGzEtm3bRI5/Uvxx+/ZtxMfHQ1lZGWJiYhATE8PQoUOxbds2eHh4ICMjg2iegIAATJ48GZqamlBTUwPDMCgtLYWJiQlOnDhBNAsAuLi4oEOHDrh06RJUVVV5McVbXV0d9fX1IusNDQ3o2bMn8TzJycm4ffs2TE1NiV/7UxwdHQU/m5mZ4dGjR8jPz4e6ujq6du1KLEdQUBDU1dWhpaWFxMTETxYiuTh6fvz4cUydOhUaGhqCHdOlpaXQ09PDL7/8QjwPH6mpqSEuLk6kaBoXF0e8DQbQ3HaitLRUpGj65MkTzgYvTpgwAcrKyvDy8kJMTAznO4bnzZuH+fPno7i4GJaWlmAYBklJSdixYwe8vLw4zUZ9nWjRlKIoiqIoiod+//13GBgYQEdHR2gibH19PW7fvo3hw4cTzbNz504MHz4c+vr6gt2JN2/exJs3bxAfH080C/XlGhsbBT0Vu3btimfPnkFfXx8aGhooKCggnkdNTQ3p6em4fv068vPzwbIsjIyMMGbMGOJZACAzMxNpaWmcDTVpy86dO7F06VIcPHgQ/fv3B8MwSE1NxbJly7B7927ieQwMDIjvbv3fkpGR4aTfo5OTEy8K7W3R1dVFdnZ2m//W+JqZNC8vL3h4eCAzM1OoABcaGop9+/YRzzNjxgy4u7tj9+7dQnm8vb0xa9Ys4nkAYM+ePfj999+xa9cu7NmzRzAMauTIkYJhfiStW7cOcnJy8PPzw5o1awAAPXr0wMaNG+Hh4UE8D/X1o8fzKYqiKIqieEhMTAzdunVDdHQ0hgwZIlivqKhAjx49ODk2/OzZMwQEBCArKwvS0tLo27cvlixZgs6dOxPPQn2ZYcOGwcvLC1OmTIGDgwNevXqFn376CYcPH0ZaWhpycnKI5ikpKYGWlhbRa37OgAEDsHfvXl7tllZSUkJtbS0aGhqE+hp26NABnTp1EnouiX6i165dw6ZNm7B161aYmJiI9DSVl5dv9wwfW758eZvrDMNASkoKurq6mDx5Mv1sov7W+fPn4efnJxiMZWhoCG9vb0yePJl4lg8fPsDb2xtBQUFoaGgA0HzqZNGiRdi+fTs6duxIPFNr9+7dQ2JiIhISEnDx4kV06dIFZWVlnOVpaQvE1S5c6r+BFk0piqIoiqJ4SExMDMuWLUNwcDAOHToEFxcXAM1FU1VVVU6H5lD/HjExMaipqYG9vT2Ki4sxceJE5Ofno0uXLjhz5gxGjRpFNI+4uDiGDx8Od3d3fPfdd5CSkiJ6/Y/Fx8fjp59+gu//x96dx+WY7/8Df92lqLQi26RVKKUsDUULxqRGYmZkZCv7oJBtzhz7coyxrzGH0lhDyHJsbRTGEoVJKiWDrEOUqLv794df99ftjmHO6bouvJ6PxzxOfa77ca7Xgxb3+3p/3p+5cyVTENywYcM7v1aIeaIVhxm93p0o5txXLy8vpKamQi6Xo0mTJlAoFMjKyoKmpiaaNm2KzMxMZZeenZ2d4PmkIi4uDnFxcZUecrZ+/XqRUtFfKS4uRk5ODhQKBWxsbKCrqyt2JJw/fx6JiYlISEjA8ePH8eTJEzg7O+PMmTNiRyOqUiyaEhEREUmQpqYmbt++jeTkZAwYMABDhgzBwoULcffuXdE6Tenj8PDhQxgbG4uyRffSpUtYv349Nm3ahOfPnyMgIADBwcH4/PPPBc8CSLMgKDV/dWCQh4eHQEn+z5IlS3D8+HFEREQoC9uFhYUYNGgQ2rdvjyFDhqBPnz549uwZDh06JHg+KZgxYwZmzpyJ1q1bVzqvV+hD16TIysoKZ86cUTu869GjR2jZsiWuXbsmaJ7g4GAsXbpUrXOyqKgIo0ePFqXQ7efnh+TkZBQWFsLJyUm5Nd/d3V2Uh0p37tzB+PHjlQ8DXi9n8Wc2/a+xaEpEREQkQRoaGigoKICpqSnOnz+P7t27w87ODkuXLoWdnR3fGNAHraysDHv37kVkZCT+85//oHHjxhg0aBD69euHOnXqCJZDigVB4OUb/927dyMjIwMymQx2dnbw8/NTHsImlNLSUnTp0gVr1qyBra2toPd+m4YNG+LIkSNqXaSXL19Gly5dcPPmTaSmpqJLly64f/++SCnFVb9+fcyfPx/9+vUTO4qS1DpfX/09+6o7d+6gUaNGeP78uaB5Kh6Wvp7n/v37qFevnnLLvpDGjx8vapH0dV27dkV+fj5GjRpV6cMAMcYq0MeNB0ERERERSZyzszNOnz4Nf39/dOrUSew4RP+1atWqoUePHvDx8cGqVavwww8/YPz48fjhhx8QEBCAn376CfXr16/yHGIVRd8mOzsbPj4+uHnzpnLr+dWrV2FmZob9+/erHAxX1bS0tHDp0iXJHRz0+PFj3L17V61oeu/ePRQWFgIAjIyM8OLFCzHiScKLFy/g6uoqdgylv+p8FVJsbKzy49dPhJfL5YiLi4OFhYVgeQoLC6FQKKBQKPDkyROVsSVyuRwHDhxQK6QKRYzD594mOTkZx48fh5OTk9hR6BPBTlMiIiIiCQoKCsKyZctUtuk9f/4cQ4cOxbFjx5CbmytiOqL/ztmzZ7F+/Xps3boVenp6GDBgAAYNGoRbt25h6tSpePLkCU6fPi1YnuLiYuTn56sV2RwdHQXLUMHHxwcKhQKbNm1SHmT04MED9O3bFxoaGti/f7+gecLCwqClpYV58+YJet+3CQwMxMmTJ7Fw4UK0adMGMpkMp0+fxvjx4+Hq6opff/0VW7duxYIFC3D27Fmx44pi0qRJqFmzJqZMmSJ2FADS6nx9dSzH6+UQLS0tWFhYYOHChfjqq68Ey/O2IrJMJsOMGTPw448/CpLndUlJSViwYIGy873isKwOHToInsXOzg6bNm2Cs7Oz4PemTxOLpkRERET0zu7du6c8ZMXW1lbQrdT04Vu0aBEiIiKQmZkJHx8fDB48GD4+PsoiBvCy07Jp06aCbEW9d+8egoKC8J///KfS62KMwdDT08OpU6fg4OCgsp6WlgY3Nzc8ffpU0DyjR49GVFQUbGxs0Lp1a+jp6alcX7RokaB5AODp06cYO3YsoqKilF8n1apVw4ABA7B48WLo6enhwoULAPDJdqSFhoYiKioKjo6OcHR0VDvkTOi/t1q1auH06dOCdkr/FUtLS5w5cwa1a9cWNUdSUhIUCgU6duyInTt3Kh+WAIC2tjbMzc3RoEEDUbJt3LgRQUFB6NmzJ9zc3KBQKHDixAns2rULkZGR6NOnj6B5Dh8+jIULF2LNmjWCdgPTp4tFUyIiIiKJSE9PR/PmzaGhoYH09PS3vlboDriKgyh+/fVXZSFJU1MT/fv3x/LlyyVxui+pO3bsGFxdXVGtmupUrrKyMpw4cQLu7u6C5mncuDGCg4MRFBSEevXqVfqaFy9eYMuWLYKcDB8YGIi8vDwsWbIEXl5e2LVrF+7cuYPZs2dj4cKF8PX1rfIMrzMxMcG+ffvUtlanpKSgW7duePjwoaB5vLy83nhNJpMhPj5ewDSqnj59imvXrkGhUMDa2ho1a9YULYvUSO3vTWqdr1J0/fp1mJmZqTxEEluzZs0wdOhQjB07VmV90aJF+OWXX5CRkSFoHmNjYxQXF6OsrAy6urpqDwOE/vlIHz8WTYmIiIgk4tVDKSq26736T7WKz8U41XvYsGE4evQoVqxYATc3NwAvZ4uFhITgiy++wOrVqwXNQ+/mTQeLPHjwAKampp/8gWL169fHnj174OLiAgMDA5w9exa2traIjY3F/PnzkZycLHim/v37IzU1FevWrYOLiwsA4LfffsOQIUPQqlUrREZGCp5JqrKzs5GTkwN3d3fo6Ogofz6S9Eit87WC1A6nevToEU6fPl1pnv79+wuep3r16rh8+TJsbGxU1rOzs9G8eXOUlJQImmfDhg1vvS7Ewzb6tPAgKCIiIiKJyM3NVW53l9rM0p07d2LHjh3w9PRUrvn4+EBHRwe9evVi0VSi3lREevDggdo2609RUVGRsqBsYmKCe/fuwdbWFg4ODkhNTRUl07JlyzBgwAC0a9dOWVgqKyuDn58fli5dKkqmCn/88QdkMhkaNmwoao4HDx6gV69eSEhIgEwmQ1ZWFqysrDB48GAYGRlh4cKFouYjdenp6cpRCZcuXVK5JlahW0qHUwHA3r17ERgYiKKiIujr66vkkclkohRNzczMEBcXp1Y0jYuLg5mZmeB5WBQlobFoSkRERCQR5ubmAIDS0lJMnz4dU6ZMgZWVlcipXiouLkbdunXV1k1NTVFcXCxCInqbnj17Anj5RnvgwIGoXr268ppcLkd6erqkTtYWS5MmTZCZmQkLCws4OTkp5+SFh4ejfv36gudRKBR4/PgxtmzZglu3biEjIwMKhQJ2dnZqRQuhlJeXK8cVVMxT1dfXR1hYGH788UdRthKPHTsWWlpayM/PR7NmzZTrAQEBGDt2LIum/9+ZM2ewffv2Sg85i4mJETRLQkKCoPd7F+Hh4YiMjJTE4VTAy0PXgoODMXfuXMmMvAkLC0NISAguXLgAV1dXyGQyJCcnIzIyUvSHOERCYNGUiIiISGK0tLSwa9cuSc1+a9euHaZNm4aoqCjUqFEDAPDs2TPMmDED7dq1Ezkdvc7Q0BDAyyKcvr4+dHR0lNe0tbXRtm1bDBkyRKx4kjFmzBjcvn0bADBt2jR8+eWX2LRpE7S1tUXZBq9QKNC4cWNcvnwZjRs3Fq1Q+qoff/wR69atw7x585QHwaSkpGD69OkoKSnBnDlzBM90+PBhHDp0CJ999pnKeuPGjXH9+nXB80jR1q1b0b9/f3Tp0gVHjhxBly5dkJWVhYKCAvTo0UPUbFLpWH7x4oWkHh7dvHkTISEhkimYAsCIESNQr149LFy4ENHR0QBezjndtm0bunfvLnI6oqrHmaZEREREEhQUFAQHBweMGzdO7CgAgIsXL6Jr164oKSlBixYtIJPJcOHCBdSoUQOHDh2Cvb292BHpNQqFAkFBQVi+fDn09fXFjvNBKC4uxpUrV9CoUSPRTtS2t7fHunXr0LZtW1Hu/7oGDRogPDwcfn5+Kut79uzB999/j5s3bwqeSV9fH6mpqWjcuDH09fWRlpYGKysrnDlzBt7e3njw4IHgmaTG0dERw4YNw8iRI5V/RpaWlhg2bBjq16+PGTNmCJpHih3LUjucqmfPnujduzd69eoldhQi+v/YaUpEREQkQTY2Npg1axZOnDiBVq1aqc2fDAkJETSPg4MDsrKysHHjRly5cgUKhQK9e/dGYGCgShcjSYdCocDmzZvx448/smj6jnR1ddGyZUtRM8yfPx8TJkzA6tWr0bx5c1GzAC9Po27atKnaetOmTUU7qdrd3R1RUVGYNWsWgJdjKMrLy/Hzzz+/9dT4T0lOTg58fX0BvDzMp6ioCDKZDGPHjkXHjh0FL5pKsWO5pKQEa9euxdGjRyVxOJWvry8mTJiA33//HQ4ODmp5Xn9wIaQXL15UejhVo0aNREpEJAx2mhIRERFJkKWl5RuvyWQyXLt2TcA0wLFjx+Dq6opq1VSfuZeVleHEiRNwd3cXNA+9G6l1Ld65cwfjx49Xnlb9+lsRuVwuUjLpMDY2RnFxMcrKyqCtra32UELoQuXnn3+Ozz//HMuWLVNZHz16NM6cOYNTp04JmgcAfv/9d3h6eqJVq1aIj4+Hn58fLl++jIcPHyIlJQXW1taCZ5IaMzMzHDhwAA4ODmjRogUmT56M7777DidPnoS3tzceP34saB4pdiy/rcAuk8kQHx8vYBq8tdtWJpOJ8vMxKysLwcHBOHHihMp6xSGD/JlNHzt2mhIRERFJUG5urtgRVHh5eeH27dvKk8YrPH78GF5eXnzjJFFS61ocOHAg8vPzMWXKFEmcVi1FS5YsETuCivnz58PX1xdHjx5Fu3btIJPJcOLECdy4cQMHDhwQJZOdnR3S09OxevVqaGpqoqioCD179sTIkSNFOcBLijp06IAjR47AwcEBvXr1QmhoKOLj43HkyBF06tRJ8DxS7FiW2uFUr3dxSsHAgQNRrVo17Nu3TxI/s4uKijBv3jzlg7fX/8yEfqBMHz92mhIRERFJXMU/18R8s6KhoYE7d+6gTp06KutXr15F69atUVhYKFIyehupdS3q6+vj+PHjcHJyEvS+9N+5desWVq5cqRzNYWdnh++//x4NGjQQO5qKGzduYNq0aVi/fr3YUUT38OFDlJSUoEGDBigvL8eCBQuQnJwMGxsbTJkyBcbGxoLmkWLHMv01PT09nDt3rtKCtxi+++47JCUloV+/fpUWcUNDQ0VKRh8rFk2JiIiIJCoqKgo///wzsrKyAAC2traYMGEC+vXrJ1iGnj17Ani5hdLb2xvVq1dXXpPL5UhPT0eTJk1w8OBBwTLRu9uwYcNbrw8YMECgJC/Z2dlh06ZNcHZ2FvS+Hxq5XI5du3YhIyMDMpkMzZo1Q/fu3dXGY1SVnj17IjIyEgYGBoiKikJAQIDK975UpaWloWXLlux8l6CkpCT4+vqiUaNGlXYsd+jQQZAcr35tV/x+e5OYmJgqz7Ns2TIMHToUNWrUUCsov07oWeYA0KZNGyxevBjt27cX/N6VMTIywv79++Hm5iZ2FPpEcHs+ERERkQQtWrQIU6ZMwahRo1QOzRg+fDju37+PsWPHCpLD0NAQwMtuV319fZVORW1tbbRt2xZDhgwRJAu9P6GLon9lyZIlmDx5MtasWQMLCwux4+DgwYOoWbOmsiCwcuVK/PLLL7Czs8PKlSsF78YDgEuXLqF79+4oKChAkyZNALzs6K5Tpw5iY2Ph4OBQ5Rn27duHoqIiGBgYICgoCN7e3mqjOYjeh4eHB65evarSsdyzZ0/BO5YNDQ2V3YkVv9/EtHjxYgQGBqJGjRpYvHjxG18nk8kEK5q+unPkp59+wsSJEzF37txKD6cyMDAQJFMFY2NjmJiYCHpP+rSx05SIiIhIgiwtLTFjxgz0799fZX3Dhg2YPn264DNPZ8yYgfHjx0NPT0/Q+9J/Ty6XY/fu3cquRTs7O/j5+UFTU1OQ+xsbG6tsoSwqKkJZWRl0dXXV3oALPS7AwcEBP/30E3x8fHDx4kW0adMG48aNQ3x8PJo1a4aIiAhB8wBA27ZtYWpqig0bNiiLtn/++ScGDhyIu3fv4uTJk1WewdHRES1btoSXlxeCgoKwbNmyNxZHXv8ZJSZ2mhL99zQ0NFR+Zlcc+vQqsQ6C2rhxI/bs2YMNGzZAV1dX0HvTp4lFUyIiIiIJqlGjBi5dugQbGxuV9aysLDg4OKCkpESkZPQhyc7Oho+PD27evIkmTZpAoVDg6tWrMDMzw/79+wU5ZfyvRgS8SujO2Jo1a+LSpUuwsLDA9OnTcenSJezYsQOpqanw8fFBQUGBoHkAQEdHB2fPnoW9vb3K+qVLl9CmTRs8e/asyjOcOHEC48aNQ05ODh4+fAh9ff1KZyrLZDLRDvGpDIum0pKeno7mzZtDQ0MD6enpb32to6OjQKnoryQlJb3zaz08PKowiTpnZ2fk5ORAoVDAwsJC7cFbamqqoHno48ft+UREREQSZGNjg+joaPzjH/9QWd+2bRsaN24sUir60ISEhMDa2hqnTp1Sbml88OAB+vbti5CQEOzfv7/KM0htRMCrtLW1UVxcDAA4evSosmvSxMREtMPNmjRpgjt37qgVTe/evav2EKWquLq6Kg/m0dDQwNWrVyWxPf+vZlA+evRImCD0TpycnFBQUABTU1M4OTlBJpOhsp4tMToW6c2ELoS+D39/f7Ej0CeGRVMiIiIiCZoxYwYCAgJw7NgxuLm5QSaTITk5GXFxcYiOjhY7Hn0gkpKSVAqmAFCrVi3MmzdPlIM0NDU1cfv2bbUC3IMHD2Bqaip44aR9+/YYN24c3NzccPr0aWzbtg3Ayxmin332maBZKsydOxchISGYPn062rZtCwA4deoUZs6ciZ9++kmlmCvEPMHc3FzUqVOnyu/zLv5qBqWhoaGkxgWIKTg4GEuXLoW+vr7KelFREUaPHo3169dXeYZXv3aEHilD/xtSm/s8bdo0Qe9HxO35RERERBJ17tw5LF68GBkZGVAoFLCzs0NYWBhPHqd3ZmJign379sHV1VVlPSUlBd26dRN8a7WGhoay8+xVt27dgrW1tSBbz1+Vn5+P77//Hjdu3EBISAgGDRoEABg7dizkcvlfnmZdFTQ0NJQfV2yJr3jL9urnQnbnPXr0CKdPn8bdu3dRXl6uco1FSml60wOK+/fvo169eigrKxM0z7Fjx+Dq6opq1VT7tsrKynDixAm4u7sLmofezetzn1u3bo2wsDBR5z4/evQIO3bsQE5ODiZMmAATExOkpqaibt26aNiwoeB56OPGoikRERER/S2PHj2CkZGR2DHoLfr374/U1FSsW7cOLi4uAIDffvsNQ4YMQatWrRAZGSlIjori49ixYzFr1izUrFlTeU0ul+PYsWPIy8vD+fPnBckjZVKbJ7h3714EBgaiqKhIbbap1Gaa0suTzxUKBYyNjZGVlaXSJSyXy7F3715MnjwZt27dEjSX1LrM6d1Ibe5zeno6OnfuDENDQ+Tl5SEzMxNWVlaYMmUKrl+/jqioKEHz0MeP2/OJiIiIJKq8vBzZ2dmVdncJ3ZXz008/wcLCAgEBAQCAXr16YefOnahXrx4OHDiAFi1aCJqH3s2yZcswYMAAtGvXTnlgRllZGfz8/LB06VLBcixevBjAyw7J8PBwaGpqKq9pa2vDwsIC4eHhguV5VU5ODiIiIpCTk4OlS5fC1NQUBw8ehJmZmdpcUSFIbZ5gWFgYgoODMXfuXJ5W/QEwMjKCTCaDTCaDra2t2nWZTIYZM2YInquyE9iBl0VTPT09wfNUiIuLQ1xcXKW/Z4UYYfA6qXV1S23u87hx4zBw4EDMnz9fZfRE165d0adPH8Hz0MePnaZEREREEnTq1Cn06dMH169fVzs4Q4xDM6ysrLBx40a4urriyJEj6NWrF7Zt24bo6Gjk5+fj8OHDguah95OVlYWMjAwAgJ2dnWAHCr3Oy8sLMTExgs/Be5OkpCR07doVbm5uOHbsGDIyMmBlZYX58+fj9OnT2LFjhyi5SkpKkJ6eXmnhxM/PT9Asenp6uHjxIqysrAS9L/09SUlJUCgU6NixI3bu3Kkyz1hbWxvm5uZo0KCBYHkqDu/as2cPvL29Ub16deU1uVyO9PR0NGnSBAcPHhQsU4UZM2Zg5syZaN26NerXr69W1N21a5egeaTY1e3n54cXL17Azc0Ns2bNQm5uLho2bIjDhw9j1KhRuHr1qqB5DA0NkZqaCmtra+jr6yMtLQ1WVla4fv06mjRpgpKSEkHz0MePnaZEREREEjR8+HC0bt0a+/fvr/TNnNBu374NMzMzAMC+ffvQq1cvdOnSBRYWFvj8889FzUZ/rXHjxspCqZhfSwkJCaLduzKTJ0/G7NmzMW7cOJWuJS8vL0E7cV918OBB9O/fH/fv31e7JsYDky+//BJnz55l0fQDUdGpnJubCzMzM5UZuWKoOLxLoVBAX18fOjo6ymva2tpo27YthgwZIkq28PBwREZGol+/fqLc/3VS7OpesWIFvv/+e+zYsQOrV69Wzgz9z3/+A29vb8Hz1KhRo9IO18zMTMkcWEcfFxZNiYiIiCQoKysLO3bsEK0j8HXGxsa4ceMGzMzMcPDgQcyePRvAyzfCnEUnbevWrcPixYuRlZUF4GUBdcyYMRg8eLAg9x83bhxmzZoFPT09jBs37q2vXbRokSCZKly8eBGbN29WW69Tpw4ePHggaJYKo0aNwrfffoupU6eibt26omR4la+vLyZMmIDff/8dDg4OyjEPFYTufKV3Y25uLomt3hUHBVlYWGD8+PGibsV/3YsXL9QOyRPTzZs3ERISIpmCKQA0atQI+/btU1uvGLkitO7du2PmzJmIjo4G8PJBUn5+PiZPnoyvv/5alEz0cWPRlIiIiEiCPv/8c2RnZ0umaNqzZ0/06dMHjRs3xoMHD9C1a1cAwIULFySTkdRNmTIFixcvxujRo9GuXTsAwMmTJzF27Fjk5eUpi99V6fz58ygtLVV+/CZidMAaGRnh9u3bsLS0VFk/f/68aKcw3717F+PGjZNEwRSAsgtw5syZatfE6Hyld/NXW72Fno85bdo0Qe/3LgYPHozNmzdjypQpYkcBwK7ud7FgwQL4+PjA1NQUz549g4eHBwoKCtCuXTvMmTNH7Hj0EeJMUyIiIiKJSE9PV36ck5ODf/7zn5gwYUKl3V2Ojo6CZistLcXSpUtx48YNDBw4EM7OzgCAJUuWoGbNmoJ1LdL7qV27NpYvX47vvvtOZX3Lli0YPXp0pVvAPyUTJ07EyZMnsX37dtja2iI1NRV37txB//790b9/f1EKPcHBwXBzc8OgQYMEvzd9PGxtbeHj4yOprd47duxQzsF+8eKFyrXU1FTB84SGhiIqKgqOjo5wdHRU+z0rdOf7unXrMHPmTAQFBbGr+y/Ex8cjNTUV5eXlaNmyJTp37ix2JPpIsWhKREREJBEaGhqQyWRqBz9VqLgmRnfXsWPH4OrqimrVVDcqlZWV4cSJE3B3dxc0D70bY2NjnD59Go0bN1ZZv3r1KlxcXPDo0SNB8xw5cgTt27dXmWsoptLSUgwcOBBbt26FQqFAtWrVIJfL0adPH0RGRkJTU1PwTMXFxfj2229Rp06dSgsnISEhgmeiD4/UDvBatmwZfvzxRwwYMAC//PILgoKCkJOTgzNnzmDkyJGidAl6eXm98ZpMJkN8fLyAafDW+bPs6n4pKioKAQEBKgeKAS9HLWzdulXwDmr6+LFoSkRERCQR169ff+fXmpubV2ESdZqamrh9+zZMTU1V1h88eABTU1O+mZOo0aNHQ0tLS61javz48Xj27BlWrlwpaB4DAwM8f/4crVq1goeHBzw9PeHm5oaaNWsKmuN1165dU3YtOTs7qxWZhfTvf/8bw4cPh46ODmrVqqW2rfratWtVnmHZsmUYOnQoatSogWXLlr31tSziSlPPnj3Ru3dv9OrVS+woAICmTZti2rRp+O6771ROPZ86dSoePnyIFStWiB2RPgD8twgJjUVTIiIiIgmSWmenhoYG7ty5o3Y67dWrV9G6detKT7Ml8Y0ePRpRUVEwMzND27ZtAQCnTp3CjRs30L9/f5UuRiG2osrlcpw+fRpJSUlITEzEiRMnUFJSgpYtW8LT0xPz5s2r8gyvmjlzJsaPH6+2ffnZs2f4+eefMXXqVEHzAEC9evUQEhKCyZMni3byuaWlJc6ePYtatWqpzXt9lVBFXHp/Utvqrauri4yMDJibm8PU1BRHjhxBixYtkJWVhbZt24p28FqFP/74AzKZTLRZxlKXnZ2NnJwcuLu7Q0dHR7nrRWhv+rdIWloavLy88PDhQ8Ez0ceNRVMiIiIiCZJKN0XPnj0BAHv27IG3t7fKlji5XI709HQ0adIEBw8eFCQPvZ+3bT99lRhbUQHg0qVLWLBgATZt2oTy8nLBu4Sk8n32KhMTE5w5cwbW1taC35s+HlLb6m1lZYUdO3agZcuWaNOmDQYPHoxhw4bh8OHD6N27tyjFrvLycsyePRsLFy7E06dPAQD6+voICwvDjz/+KMpDi6SkJCxYsAAZGRmQyWRo1qwZJkyYgA4dOgieBXj5szAgIADx8fGQyWTIysqClZUVBg0aBCMjIyxcuFCQHM7OzpDJZEhLS4O9vb3KA2W5XI7c3Fx4e3sjOjpakDz06aj21y8hIiIiIqG9qYvjwYMH0NPTEyyHoaGhMo++vr7KLEptbW20bdtWebo2SU9CQoLYEVRkZGQou0yTkpIgl8vRvn17LFy4EB4eHoLnedP3WVpaGkxMTATPAwADBgzAtm3b8I9//EOU+9PHoby8XOwIKjp27Ii9e/eiZcuWGDRoEMaOHYsdO3bg7NmzyodzQvvxxx+xbt06zJs3D25ublAoFEhJScH06dNRUlIi+JzVjRs3IigoCD179kRISAgUCgVOnDiBTp06ITIyEn369BE0DwCMHTsW1apVQ35+Ppo1a6ZcDwgIwNixYwUrmvr7+wMALly4gC+//FJlpIu2tjYsLCzw9ddfC5KFPi3sNCUiIiKSEKl2ds6YMQPjx48XtGBL/ztS2lpZp04djBkzBn5+frC3txc8A/DygCyZTIbHjx/DwMBA5c9CLpfj6dOnGD58uOAzX4GXM0KjoqLQokULSZzoTR++kpIS1KhRQ9QM5eXlKC8vV3YIRkdHIzk5GTY2Nhg+fDi0tbUFz9SgQQOEh4erjSrYs2cPvv/+e9y8eVPQPM2aNcPQoUMxduxYlfVFixbhl19+QUZGhqB5gJfjQg4dOoQWLVqozKLNzc2Fg4ODskNXKBs2bEDv3r3VDoIiqiosmhIRERFJSFBQEICXbwx69eql1tlpYWGBIUOGoHbt2qLku3v3LjIzMyGTyWBra6u2rZmk5cGDB+jVqxcSEhJE3VpZYcyYMTh27BguX74MJycneHp6wtPTEx06dBD0MKgNGzZAoVAgODgYS5YsUXZUA//3fdauXTvB8rxKaid604dJLpdj7ty5CA8Px507d3D16lVYWVlhypQpsLCwwKBBg8SOKLoaNWogPT0dtra2KuuZmZlwcnLCs2fPBM1TvXp1XL58GTY2Nirr2dnZaN68OUpKSgTNA7wcV5CamorGjRurFE3PnDkDb29vwWfRVty7Vq1aKuuPHj1Cy5YtOWOZ/ue4PZ+IiIhIQiIiIgAAFhYWkursLCwsxMiRI7F161blLDxNTU0EBARg5cqVKkUnko6xY8dCS0tL9K2VFZYsWQLg5Rvc48ePIykpCVOnTsXFixfh5OSEU6dOCZJjwIABAF4eeOTq6qrWzSkmqY1UoA/TnDlzsGHDBsyfP19lhIqDgwMWL14seNE0PT290nWZTIYaNWqgUaNGgncPtmjRAitWrMCyZctU1lesWIEWLVoImgUAzMzMEBcXp1Y0jYuLg5mZmeB5AMDd3R1RUVGYNWsWgJd/X+Xl5fj555/feWb2/1JeXl6l83ifP38ueGcwfRpYNCUiIiKSoGnTpokdQcXgwYNx4cIF7Nu3D+3atYNMJsOJEycQGhqKIUOG8PAFiTp8+DAOHTqEzz77TGW9cePGuH79ukipXm7VLSsrw4sXL/D8+XOUlpYiLy9P8ByWlpa4ffv2G683atRIwDTSlJ+fDzMzM7VxDgqFAjdu3OCfkURFRUVh7dq16NSpE4YPH65cd3R0xJUrVwTP4+Tk9NaRIFpaWggICMCaNWsEGyUwf/58+Pr64ujRoyq/127cuIEDBw4IkuFVYWFhCAkJwYULF+Dq6gqZTIbk5GRERkZi6dKlgucBgJ9//hmenp44e/YsXrx4gYkTJ+Ly5ct4+PAhUlJSBMsRGxur/PjQoUMqD2rlcjni4uJgYWEhWB76dHB7PhEREZEEWVpavvUNptBb0PT09HDo0CG0b99eZf348ePw9vZGUVGRoHno3Uhta2VoaCgSExNx+fJlmJiYwN3dXblFv3nz5oJmAV7OWH3b95mQJ4y/62E4MTExVZxElaamJm7fvq02iuPBgwcwNTUV/BR2ejc6Ojq4cuUKzM3NVb73f//9d7i4uAg+i3LPnj2YNGkSJkyYABcXFygUCpw5cwYLFy7EtGnTUFZWhsmTJyMgIAALFiwQLNetW7ewcuVKXLlyBQqFAnZ2dvj+++/RoEEDwTK8ateuXVi4cKFyfmmzZs0wYcIEdO/eXZQ8AFBQUIDVq1fj3LlzKC8vR8uWLTFy5EjUr19fsAwaGhoAXna6vl7C0tLSgoWFBRYuXIivvvpKsEz0aWCnKREREZEEjRkzRuXz0tJSnD9/HgcPHsSECRMEz1OrVq1Kt+AbGhrC2NhY8Dz0bqS2tfLmzZsYMmSIaEXS150/f17l84rvs0WLFgl+crZUR1y86dCwp0+fin64EL2Zvb09jh8/DnNzc5X17du3w9nZWfA8c+bMwdKlS/Hll18q1xwdHfHZZ59hypQpOH36NPT09BAWFiZo0bRBgwaCf6+/TY8ePdCjRw+xY6ioV68eZsyYIWqG8vJyAC8fKJ85c0a0ue706WHRlIiIiEiCQkNDK11fuXIlzp49K3Aa4J///CfGjRuHqKgoZXdJQUEBJkyYgClTpgieh96NVLZWVtixY4fg93ybyuYWtm7dGg0aNMDPP//8zt2f/wsV84ylYty4cQBeFtqnTJkCXV1d5TW5XI7ffvsNTk5OIqWjvzJt2jT069cPN2/eRHl5OWJiYpCZmYmoqCjs27dP8DwXL15UK+ACgLm5OS5evAjg5Rb+t43L+F9IT09H8+bNoaGh8cY5qxUcHR2rNMuH4ODBg6hZs6Zyl8nKlSvxyy+/wM7ODitXrhT8oWlubq6g9yPi9nwiIiKiD8i1a9fg5OSEwsJCQe/r7OyM7OxsPH/+XDnDMD8/H9WrV0fjxo1VXpuamipoNno7KWyt/NBkZWXBycnpkx47UdGJnJSUhHbt2kFbW1t5TVtbW3lY3evf/yQdhw4dwty5c1W+96dOnYouXboInsXZ2RktWrTA2rVrlV9LpaWlGDJkCNLS0nD+/HmkpKSgb9++VVoY09DQQEFBAUxNTZXjOSorichkMkFGT5iYmODq1auoXbs2jI2N3zou5OHDh1We53UODg746aef4OPjg4sXL6J169YICwtDfHw8mjVrJsjDnmXLlmHo0KGoUaOG2qFdrwsJCanyPPRpYacpERER0Qdkx44dMDExEfy+/v7+gt+T/jcq21pZUlKCBQsWYPz48SKlkobXHz4oFArcvn0b06dP/+SLgQkJCQCAoKAgLF26FAYGBiInovf15ZdfqmyHF9PKlSvh5+eHzz77DI6OjpDJZEhPT4dcLld2vl67dg3ff/99lebIzc1FnTp1lB+LbfHixdDX11d+/LaiqRhyc3NhZ2cHANi5cye6deuGuXPnIjU1FT4+PoJkWLx4MQIDA1GjRg0sWrTojX9GMpmMRVP6n2OnKREREZEEOTs7q7wxUCgUKCgowL1797Bq1SoMHTpUxHT0Ibh//z5+++03aGlpoVOnTtDU1ERpaSlWrVqFf/3rXygrK8P9+/fFjimqyg6CUigUMDMzw9atW9GuXTuRkknH48ePIZfL1R7WPHz4ENWqVWMx9QPw9OlT5UzICmL8vT19+hQbN27E1atXoVAo0LRpU/Tp00dZNBTasWPH4OrqimrVVHvJysrKcOLECbi7u4uSS0pMTEyQnJwMOzs7tG/fHv3798fQoUORl5cHOzs7FBcXix2RqEqxaEpEREQkQa93BmpoaKBOnTrw9PRE06ZNRUr1klTegNObnThxAr6+vnj8+DFkMhlat26NiIgI+Pv7o7y8HGPGjEFwcLDKnMpPUVJSksrnFd9nNjY2aoWUT1XXrl3RrVs3tQ7A8PBwxMbG4sCBAyIlo7fJzc3FqFGjkJiYiJKSEuV6xcFeQmw9lzpNTU3cvn0bpqamKusPHjyAqamp4H9GUssDAH5+fnjx4gXc3Nwwa9Ys5ObmomHDhjh8+DBGjRqFq1evCpaltLQUTZo0wb59+5Tdr0RVjUVTIiIiIvpLfAP+YenUqRPq1KmDf/7zn1i/fj2WLFkCCwsLTJ8+Hf369ZPcFlCSLhMTE6SkpKBZs2Yq61euXIGbmxsePHggUjJ6G1dXVwAvDxWsW7eu2ve8h4dHlWeIjY1F165doaWlhdjY2Le+1s/Pr8rzvE5DQwN37txRbtevcPXqVbRu3Vrw2eGvzlt91a1bt2BtbY1nz54Jmgd4Obv8+++/x40bNxASEoJBgwYBAMaOHQu5XP6XM0b/1xo2bIijR4+q/TwiqiosmhIRERFJ3LNnz1BaWqqyJnRnpxTegNO7q127NpKSkmBvb4/i4mLo6+tj69at+Pbbb0XN9aaDTmQyGWrUqAEbGxsMHDgQQUFBgmXKzMzE8uXLkZGRAZlMhqZNm2LUqFGid3RLhZ6eHk6dOgUHBweV9YsXL+Lzzz/n9lyJqlmzJs6dO4cmTZqIluH1Q5feROgHbz179gQA7NmzB97e3qhevbrymlwuR3p6Opo0aYKDBw8Kkqei8Dh27FjMmjULNWvWVMlz7Ngx5OXl4fz584LkkbJ58+bhypUr+Pe//83dACQIfpURERERSVBRUREmTZqE6OjoSju5hO7sTE9PF/0NOL27hw8fKrundHV1oaurC2dnZ5FTAVOnTsWcOXPQtWtXuLi4QKFQ4MyZMzh48CBGjhyJ3NxcjBgxAmVlZRgyZEiV59mxYwe+++47tG7dWjm/tKJAuHnzZtGLzFLQpk0brF27FsuXL1dZDw8PR6tWrURKRX+lTZs2uHHjhqg/s18d4/L6SBcxGRoaAni5U0JfXx86OjrKa9ra2mjbtq0gP38qLF68WJknPDwcmpqaKnksLCwQHh4uWJ43kcID3N9++w1xcXE4fPgwHBwcoKenp3I9JiZG0Dz08WPRlIiIiEiCJk6ciISEBKxatQr9+/fHypUrcfPmTaxZswbz5s0TPI8U3oDTu5PJZHjy5Alq1KihHKFQXFystt1U6De8ycnJmD17NoYPH66yvmbNGhw+fBg7d+6Eo6Mjli1bJkjRYuLEifjhhx8wc+ZMlfVp06Zh0qRJLJoCmDNnDjp37oy0tDR06tQJABAXF4czZ87g8OHDIqejN/n3v/+N4cOH4+bNm2jevDm0tLRUrjs6OgqaJyoqCgEBASpdnQDw4sULbN26Ff379xcsS0REBADAwsIC48ePVyu8CS03NxcA4OXlhZiYGBgbG4ua51VSe4BrZGSEr7/+WtB70qeN2/OJiIiIJKhRo0aIioqCp6cnDAwMkJqaChsbG/z666/YsmWL4Iev5OTkYPjw4ejbt68k3oDT271+KnxF4fT1z4V+w1uzZk1cuHABNjY2KuvZ2dlwcnLC06dPkZOTA0dHRxQVFVV5Hl1dXaSnp6vlycrKQosWLbj1/P+7cOEC5s+fj7S0NOjo6MDR0RE//PADGjduLHY0eoNTp06hT58+yMvLU67JZDLRvveleMgR/bWRI0ciISEBM2fOrPQBbmBgoCA5srOz1X5OEwmBnaZEREREEvTw4UNYWloCeNkN+PDhQwBA+/btMWLECMHz3Lt3Dzk5OSqzJsV8A05vl5CQIHaESpmYmGDv3r0YO3asyvrevXthYmIC4GVnk76+viB5PD09cfz4cbU348nJyejQoYMgGT4ETk5O2Lx5s9gx6D0EBwfD2dkZW7ZsqXQOtdBef3BT4Y8//lBulxfDjh07EB0djfz8fLx48ULlWmpqquB5/vjjD8TGxlaaZ9GiRYLn2bt3r/IBbnBwMDp06AAbGxuYm5tj06ZNghVNbW1t0bBhQ3h5eaFjx47w8vKCubm5IPemTxuLpkREREQSZGVlhby8PJibm8POzg7R0dFwcXHB3r17YWRkJHgeqb0Bp7eT6sFcU6ZMwYgRI5CQkAAXFxfIZDKcPn0aBw4cUM7sO3LkiGD5/fz8MGnSJJw7dw5t27YF8LJDb/v27ZgxY4bKid9inO4tFTk5OYiIiMC1a9ewZMkSmJqa4uDBgzAzM4O9vb3Y8agS169fR2xsrOjdec7OzpDJZJDJZOjUqZPK4T1yuRy5ubnw9vYWJduyZcvw448/YsCAAdizZw+CgoKQk5ODM2fOYOTIkYLniYuLg5+fHywtLZGZmYnmzZsjLy8PCoUCLVu2FDwPIJ0HuElJSUhKSkJiYiJGjhyJkpISNGrUSFlA9fLyQsOGDQXLQ58Obs8nIiIikqDFixdDU1MTISEhSEhIgK+vL+RyOcrKyrBo0SKEhoYKmkdPTw9paWmivwGnD19KSgpWrFiBzMxMKBQKNG3aFKNHj4arq6vgWd52overPuVu6qSkJHTt2hVubm44duwYMjIyYGVlhfnz5+P06dPYsWOH2BGpEt26dcPAgQNFn/84Y8YM5f+GhYWpnAxfccjR119/DW1tbcGzNW3aFNOmTcN3330HfX19pKWlwcrKClOnTsXDhw+xYsUKQfO4uLjA29sbM2fOVOYxNTVFYGAgvL29Rdll4ujoiOXLl8PDwwNdunSBo6MjFixYgGXLlmH+/Pn4448/BM9UWlqKkydPIjExEYmJiTh16hSeP38OGxsbZGZmCp6HPm4smhIRERF9APLz83H27FlYW1ujRYsWgt9fKm/AiUhY7dq1w7fffotx48apFJbOnDkDf39/3Lx5U+yIVIm1a9di9uzZCA4OhoODg9ocaqE7pzds2IDevXurHQQlJl1dXWRkZMDc3BympqY4cuQIWrRogaysLLRt27bSg4+qkr6+Pi5cuABra2sYGxsjOTkZ9vb2SEtLQ/fu3VXm0wpFag9wX/Xs2TMkJyfj0KFD+OWXX/D06dNP9uEWVR1uzyciIiL6ADRq1AiNGjUS7f7dunXD2LFjcfHiRUm8AacPV3l5ObKzs3H37l2Ul5erXHN3dxcp1f959OiRKCMwpOrixYuVzjOtU6eO4EUlenfDhw8HAMycOVPtmhid0x07dsS9e/fw2WefAQBOnz6NzZs3w87ODkOHDhU0S4V69erhwYMHMDc3h7m5OU6dOoUWLVogNzcXYvSW6enp4fnz5wCABg0aICcnRzn+4v79+4LnAaAyf9rLywtXrlwR7QFuSUkJTpw4gYSEBCQmJuLMmTOwtLSEh4cHVq9eLdmxNPRhY9GUiIiISKLi4uIQFxdXaXFp/fr1gmaR2htw+jBVnOh9/fp1taKEGF9HP/30EywsLBAQEAAA+Pbbb7Fz507Ur18fBw4cEKWrW2qMjIxw+/Zt5VzDCufPn+cMQQl7/XeG2Pr06YOhQ4eiX79+KCgoQOfOndG8eXNs3LgRBQUFmDp1quCZOnbsiL1796Jly5YYNGgQxo4dix07duDs2bPo2bOn4Hnatm2LlJQU2NnZwdfXF2FhYbh48SJiYmKUM5fFJtYDXA8PD5w5cwbW1tZwd3fH6NGj4eHhgbp16wqehT4t3J5PREREJEEzZszAzJkz0bp1a9SvX1/t4KVdu3aJlIw+JMHBwVi6dKnaafRFRUUYPXq04MV3Jycn2NraYsaMGZV+XQt9iraVlRU2btwIV1dXHDlyBL169cK2bduUp2kfPnxY0DxSNHHiRJw8eRLbt2+Hra0tUlNTcefOHfTv3x/9+/fHtGnTxI5IlcjNzVUrdIvJ2NgYp06dQpMmTbBs2TJs27YNKSkpOHz4MIYPH45r164Jnqm8vBzl5eXKw6mio6ORnJwMGxsbDB8+XPA5q9euXcPTp0/h6OiI4uJijB8/Xpln8eLFop0WL4UHuFpaWqhfvz78/f3h6ekJd3d31K5dW5B706eNRVMiIiIiCapfvz7mz5+Pfv36iR1FTUlJCWrUqCF2DHoHmpqauH37NkxNTVXW79+/j3r16qGsrEzQPFI7UExHRwdXr16FmZkZQkNDUVJSgjVr1uDq1av4/PPP8eeff4odUXSlpaUYOHAgtm7dCoVCgWrVqkEul6NPnz6IjIyEpqam2BGpEpqamnB3d8egQYPwzTffiP4zu2bNmrh06RIsLCzg5+cHNzc3TJo0Cfn5+WjSpAmePXsmaj6qnFQe4BYVFeH48eNITExEQkICLly4AFtbW3h4eMDT0xMeHh6oU6eOIFno08Lt+UREREQS9OLFC1FOE38TuVyOuXPnIjw8HHfu3MHVq1dhZWWFKVOmwMLCAoMGDRI7Ir2isLAQCoUCCoUCT548USmYyOVyHDhwQK2QKoTPP/8c2dnZkimaGhsb48aNGzAzM8PBgwcxe/ZsAIBCoeDICbz8c7h16xZ++eUXzJo1C6mpqSgvL4ezszMaN24sdjx6i7S0NKxfvx5hYWEYNWoUAgICMGjQILi4uIiSx97eHuHh4fD19cWRI0cwa9YsAMCtW7dQq1YtUTKlp6dXui6TyVCjRg00atRIUgdXiSE8PByRkZGiP8DV09ODt7c3vL29AQBPnjxBcnIyEhISMH/+fAQGBqJx48a4dOmSqDnp48OiKREREZEEDR48GJs3b8aUKVPEjgIAmDNnDjZs2ID58+djyJAhynUHBwcsXryYRVOJMTIygkwmg0wmg62trdp1mUyGGTNmCJ5r9OjRCAsLQ0FBQaUHijk6Ogqap2fPnujTpw8aN26MBw8eoGvXrgCACxcuSKawKyaFQoHGjRvj8uXLaNy4MaysrMSORO+oefPmWLRoEebPn4+9e/ciMjIS7du3R+PGjTFo0CD069dP0M68n376CT169MDPP/+MAQMGKOcFx8bGilbIdXJyUuucfJWWlhYCAgKwZs0aQTp1jY2NK81TUcS1sbHBwIEDERQUVOVZKkjtAW4FPT09mJiYwMTEBMbGxqhWrRoyMjLEjkUfIW7PJyIiIpKg0NBQREVFwdHREY6OjmrFpUWLFgmax8bGBmvWrEGnTp2gr6+PtLQ0WFlZ4cqVK2jXrh23MUtMUlISFAoFOnbsiJ07d8LExER5TVtbG+bm5mjQoIHguTQ0NNTWZDIZFAqFKAdBlZaWYunSpbhx4wYGDhwIZ2dnAMCSJUtQs2ZNDB48WNA8UmRvb49169ZJ5iAa+nueP3+OVatW4YcffsCLFy+UBcGffvoJ9evXFySDXC5HYWEhjI2NlWt5eXnQ1dUVpfN9z549mDRpEiZMmAAXFxcoFAqcOXMGCxcuxLRp01BWVobJkycjICAACxYsqPI8ixcvxpw5c9C1a1eVPAcPHsTYsWORm5uLX3/9FcuXL1d5eFmVJk2ahJo1a4r+ALe8vBxnz55Vbs9PSUlBUVERGjZsCC8vL+V/Ys19pY8Xi6ZEREREEuTl5fXGazKZDPHx8QKmeTn78cqVKzA3N1cpmv7+++9wcXHB06dPBc1D7+b69eto1KjRW7uphHT9+vW3XucbXunZv38/5s2bh9WrV6N58+Zix6H3dPbsWaxfvx5bt26Fnp4eBgwYgEGDBuHWrVuYOnUqnjx5gtOnTwuSpaysDImJicjJyUGfPn2gr6+PW7duwcDAADVr1hQkw6tcXFwwa9YsfPnllyrrhw4dwpQpU3D69Gns3r0bYWFhyMnJqfI8X3/9Nb744gsMHz5cZX3NmjU4fPgwdu7cieXLl2Pt2rW4ePFileUYN26c8uPy8nJs2LBB9Ae4BgYGKCoqQv369eHp6QlPT094eXnB2tpakPvTp4tFUyIiIiL6S61bt8aYMWPQt29flaLpjBkzcPToURw/flzsiPT/paeno3nz5tDQ0HjjzL4KQm+Hl4LY2Fh07doVWlpaiI2Nfetr/fz8BEolXcbGxiguLkZZWRm0tbWho6Ojcv3hw4ciJaO3WbRoESIiIpCZmQkfHx8MHjwYPj4+Kt3e2dnZaNq0qSAHwl2/fh3e3t7Iz8/H8+fPlXOxx4wZg5KSEoSHh1d5htfp6Ojg/PnzaNq0qcr6lStX4OzsjGfPniEvLw92dnYoLi6u8jw1a9asdDRIdnY2nJyc8PTpU+Tk5MDR0RFFRUVVluNtD21fJeQD3DVr1sDLy6vScTNEVYkzTYmIiIjoL02bNg39+vXDzZs3UV5ejpiYGGRmZiIqKgr79u0TOx69wsnJCQUFBTA1NVXO7KusT0Ko7fBSK1L6+/sr/3z8/f3f+DoxxgVI0ZIlS8SOQH/D6tWrERwcjKCgINSrV6/S1zRq1Ajr1q0TJE9oaChat26NtLQ0lYOfevToIdoYjKZNm2LevHlYu3YttLW1Abwc2TFv3jxlIfXmzZuoW7euIHlMTEywd+9ejB07VmV97969yhErRUVF0NfXr9IcCQkJVfr//3cMGzZM7Aj0iWLRlIiIiEhCvLy8Kt1KbWhoiCZNmmDkyJEwMzMTPFe3bt2wbds2zJ07FzKZDFOnTkXLli2xd+9efPHFF4LnoTfLzc1VHvCSm5srchrpFSnLy8sr/ZjUlZaWIjExEVOmTOEhUB+YrKysv3yNtrY2BgwYIEAaIDk5GSkpKcriZAVzc3PcvHlTkAyvW7lyJfz8/PDZZ5/B0dERMpkM6enpkMvlyoeB165dw/fffy9InilTpmDEiBFISEiAi4sLZDIZTp8+jQMHDig7cY8cOQIPDw9B8gDA48ePIZfLVeZiAy87zKtVqwYDAwPBshCJgdvziYiIiCTk9Q6TCo8ePcK5c+dw7do1JCcnw8nJSdhgRPRJMjIyQmpqKoumH6ji4mLk5+fjxYsXKutCj+YwMTFBcnIy7OzsVEa8JCcn4+uvv8adO3cEzVPh6dOn2LhxI65evQqFQoGmTZsq562KISUlBStWrEBmZqYyz+jRo0U7wb5r167o1q2bWuE4PDwcsbGxOHDggCi5iITCoikRERHRB2TkyJHIzc0V/I2KlZUVzpw5o7KtEnhZzG3ZsiWuXbsmaB56s7/aAv+qT3lmZ3l5OSIjIxETE4O8vDzIZDJYWlrim2++Qb9+/SRzeJbYgoKC4ODgoHI4DEnfvXv3MHDgQBw8eLDS60KPnggICIChoSHWrl0LfX19pKeno06dOujevTsaNWqEiIgIQfPQuzExMUFKSgqaNWumsn7lyhW4ubnhwYMHIiUjEga35xMRERF9QIYNG6Z20q8Q8vLyKn2T/fz5c9G2VlLlXt8C//pM01eLgUIUTpYtW/bOrw0JCanCJP9HoVDAz88PBw4cQIsWLeDg4ACFQoGMjAwMHDgQMTEx2L17tyBZpM7GxgazZs3CiRMn0KpVK+jp6alcF+rvjN7PmDFj8OjRI5w6dQpeXl7YtWsX7ty5g9mzZ2PhwoWC51m8eDG8vLxgZ2eHkpIS9OnTB1lZWahduza2bNkiWA6pzVguLCxUbnEvLCx862vF2Ar//PnzSg8KKy0txbNnzwTPQyQ0dpoSERERfUCysrLg4uKCP//8U5D7Vbyp9Pf3x4YNG2BoaKi8JpfLERcXhyNHjiAzM1OQPPR+jh49ikmTJmHu3Llo164dZDIZTpw4gX/+85+YO3euIPNoLS0t3+l1MplMsI7liIgIhIaGYs+ePWonRcfHx8Pf3x8rVqxA//79BckjZW/7+xPy74zeT/369bFnzx64uLjAwMAAZ8+eha2tLWJjYzF//nwkJycLnunZs2fYsmULUlNTUV5ejpYtWyIwMBA6OjqCZdDQ0FDOWNbQ0Hjj64SasaypqYnbt28r81TW4a5QKEQ7mM7T0xMODg5Yvny5yvrIkSORnp6O48ePC56JSEgsmhIRERF9QFauXImoqCj89ttvgtzvbW8qtbS0YGFhgYULF+Krr74SJA+9n+bNmyM8PBzt27dXWT9+/DiGDh2KjIwMkZKJq0uXLujYsSMmT55c6fW5c+ciKSkJhw4dEjgZ0f+GgYEB0tPTYWFhAQsLC2zatAlubm7Izc2Fvb09iouLBc1TXFwMXV1dQe/5IUhKSoKbmxuqVauGpKSkt75WyAOgKqSkpKBz585o06YNOnXqBACIi4vDmTNncPjwYXTo0EHwTERC4vZ8IiIiIgl503bBx48f48yZM1i3bh0iIyMFy1NxurilpSXOnDmD2rVrC3Zv+u/l5OSodAdXMDQ0RF5envCBJCI9PR3z589/4/WuXbu+11iBT0VFvw3nvUpfkyZNkJmZCQsLCzg5OWHNmjWwsLBAeHg46tevL3geU1NT+Pv7o1+/fvjiiy/e+kBOKFFRUQgICED16tVV1l+8eIGtW7cK0mn+aiHU0tISZmZmat9fCoUCN27cqPIslXFzc8OpU6cwf/58REdHQ0dHB46Ojli3bh0aN24sSiYiIbHTlIiIiEhC3vRGUl9fH02bNsX48ePx7bffCpxKGm8u6f25u7tDS0sLGzduVBZKCgoK0K9fP7x48eIvO5uqwh9//IHY2NhKT/RetGiRIBm0tbVx/fr1NxaPbt26BUtLSzx//lyQPFIXFRWFn3/+GVlZWQAAW1tbTJgwAf369RM5Gb3Jpk2b8OLFCwQFBeH8+fP48ssv8eDBA2hrayMyMhIBAQGC5omJicGWLVuwf/9+GBgYICAgAH379kWbNm0EzfGqV7fGv+rBgwcwNTUVfDu81PKUlpZi6NChmDJlCqysrAS9N5FUsGhKRERERH9Jam/m6N1kZ2ejR48eyMzMRKNGjQAA+fn5sLW1xe7du2FjYyNonri4OPj5+cHS0hKZmZlo3rw58vLyoFAo0LJlS8THxwuSQ1NTEwUFBahTp06l1+/cuYMGDRrw6xovC9lTpkzBqFGj4ObmBoVCgZSUFKxcuRKzZ8/G2LFjxY5I76C4uBhXrlxBo0aNRN0x8OTJE+zYsQNbtmxBQkICLC0t0bdvX0ydOlXwLBoaGrhz547az4G0tDR4eXnh4cOHkshz/fp12NnZoaioSNA8AGBkZITU1FQWTemTxaIpEREREf0lqb25pHenUChw5MgRXLlyBQqFAnZ2dujcubMoW6xdXFzg7e2NmTNnQl9fH2lpaTA1NUVgYCC8vb0xYsQIQXJoaGiga9euap3TFZ4/f46DBw+yaIqXW4ZnzJih1k2+YcMGTJ8+Hbm5uSIlo8oUFxdjwoQJ2L17N0pLS9G5c2csW7ZMkqNVfv/9dwQGBiI9PV3Q7zVnZ2fIZDKkpaXB3t4e1ar939RCuVyO3NxceHt7Izo6WpA848aNAwAsXboUQ4YMUZn9KpfL8dtvv0FTUxMpKSmC5HlVUFAQHBwclBmJPjWcaUpEREREb1Tx5lImk6FTp05vfHNJ0iWTydClSxe4u7ujevXqos6jzMjIwJYtWwAA1apVw7Nnz1CzZk3MnDkT3bt3F6xoOmDAgL98DUdOvHT79m24urqqrbu6uuL27dsiJKK3mTZtGiIjIxEYGIgaNWpgy5YtGDFiBLZv3y52NABASUkJYmNjsXnzZhw8eBCmpqYYP368oBn8/f0BABcuXMCXX36JmjVrKq9pa2vDwsICX3/9tWB5zp8/D+DlA66LFy9CW1tbJU+LFi0E/zOqYGNjg1mzZuHEiRNo1aoV9PT0VK6HhISIkotIKCyaEhEREdEbSe3NJb2f8vJyzJkzB+Hh4bhz5w6uXr0KKysrTJkyBRYWFhg0aJCgefT09JRzQhs0aICcnBzY29sDAO7fvy9YjoiICMHu9aGzsbFBdHQ0/vGPf6isb9u2jQfBSFBMTAzWrVuH3r17AwD69u0LNzc3yOVyaGpqipbr8OHD2LRpE3bv3g1NTU188803OHTokCgnwk+bNg0AYGFhgd69e7+x41woCQkJAF52dS5duhQGBgai5nnVv//9bxgZGeHcuXM4d+6cyjWZTMaiKX30uD2fiIiIiP7Shg0bEBAQgBo1aqhdu3DhApycnIQPRX9p5syZ2LBhA2bOnIkhQ4bg0qVLsLKyQnR0NBYvXoyTJ08Kmsff3x++vr4YMmQIJk6ciF27dmHgwIGIiYmBsbExjh49Kmge+ms7d+5EQEAAOnfuDDc3N8hkMiQnJyMuLg7R0dHo0aOH2BHpFdra2sjNzUXDhg2Vazo6Orh69SrMzMxEy6WrqwtfX18EBgbC19cXWlpaomWpcOPGDchkMnz22WcAgNOnT2Pz5s2ws7PD0KFDRU4HFBYWIj4+Hk2bNkXTpk3FjkP0SWLRlIiIiIje2+PHj7Fp0yb8+9//RlpaGmc/SpSNjQ3WrFmDTp06KWeIWllZ4cqVK2jXrh3+/PNPQfNcu3YNT58+haOjI4qLizF+/HgkJyfDxsYGixcvhrm5uaB56N2cO3cOixcvRkZGhnIublhYGJydncWORq+p7JAzfX19pKenw9LSUrRchYWFkuqgBIAOHTpg6NCh6NevHwoKCmBra4vmzZvj6tWrCAkJEfxwql69esHd3R2jRo3Cs2fP0KJFC+VBeVu3buWuDiIRcHs+ERERkUQUFha+82vFevMZHx+P9evXIyYmBubm5vj666+xbt06UbLQX7t58yZsbGzU1svLy1FaWip4nldPYNbV1cWqVasEz0Dvr1WrVti4caPYMegdKBQKDBw4UGXLeUlJCYYPH64yjzImJkbQXAYGBigvL0d2djbu3r2L8vJylevu7u6C5gGAS5cuwcXFBQAQHR0NBwcHpKSk4PDhwxg+fLjgRdNjx47hxx9/BADs2rULCoUCjx49woYNGzB79mxRiqbBwcFvvb5+/XqBkhCJg0VTIiIiIokwMjJ650N6hOzs/OOPPxAZGYn169ejqKgIvXr1QmlpKXbu3Ak7OzvBctD7s7e3x/Hjx9U6OLdv3y5al+CjR4+wY8cO5OTkYMKECTAxMUFqairq1q2rsqWYpOHAgQPQ1NTEl19+qbJ+6NAhlJeXo2vXriIlo8pUdshZ3759RUii6tSpU+jTpw+uX7+O1ze7ymQyUXYrlJaWKovLR48ehZ+fHwCgadOmohxy9vjxY5iYmAAADh48iK+//lo51mDChAmC5wGgthuhtLQUly5dwqNHj9CxY0dRMhEJiUVTIiIiIomoOAwCAPLy8jB58mQMHDgQ7dq1AwCcPHkSGzZswL/+9S/BMvn4+CA5ORlfffUVli9fDm9vb2hqaiI8PFywDPT3TZs2Df369cPNmzdRXl6OmJgYZGZmIioqCvv27RM8T3p6Ojp37gxDQ0Pk5eVhyJAhMDExwa5du3D9+nVERUUJnonebvLkyZg3b57aukKhwOTJk1k0lRipHnI2fPhwtG7dGvv370f9+vXf+QFhVbK3t0d4eDh8fX1x5MgRzJo1CwBw69Yt1KpVS/A8ZmZmOHnyJExMTHDw4EFs3boVwMvCZWXzxIWwa9cutbXy8nJ8//33KjsHiD5WnGlKREREJEGdOnXC4MGD8d1336msb968GWvXrkViYqIgOapVq4aQkBCMGDFC5aRsLS0tpKWlsdP0A3Do0CHMnTsX586dQ3l5OVq2bImpU6eiS5cugmfp3LkzWrZsifnz56vMWD1x4gT69OmDvLw8wTPR2+no6CAjIwMWFhYq63l5ebC3t0dRUZE4weiDoqenh7S0tErHhYglMTERPXr0QGFhIQYMGKDcav6Pf/wDV65cEXyEwapVqxAaGoqaNWuiUaNGOH/+PDQ0NLB8+XLExMSoPFgVW2ZmJjw9PUXpyCUSEjtNiYiIiCTo5MmTlXZztm7dGoMHDxYsx/Hjx7F+/Xq0bt0aTZs2Rb9+/RAQECDY/envKysrw5w5cxAcHIykpCSx4wAAzpw5gzVr1qitN2zYEAUFBSIkor9iaGiIa9euqRVNs7OzVWZkEr3N559/juzsbEkVTT09PXH//n0UFhbC2NhYuT506FDo6uoKnuf777+Hi4sLbty4gS+++AIaGhoAXs6Cnj17tuB53iYnJwdlZWVixyCqciyaEhEREUmQmZkZwsPDsXDhQpX1NWvWwMzMTLAc7dq1Q7t27bB06VJs3boV69evx7hx41BeXo4jR47AzMwM+vr6guWhd1etWjX8/PPPlc44FEuNGjUqPfAsMzNT5bRvkg4/Pz+MGTMGu3btgrW1NYCXBdOwsDDlDEiivzJ69GiEhYWhoKAADg4O0NLSUrnu6OgoSi6FQoFz584hJycHffr0gb6+PrS1tUUpmgIvH4w6OjoiNzcX1tbWqFatGnx9fUXJAgDjxo1T+VyhUOD27dvYv3+/pH63EFUVbs8nIiIikqADBw7g66+/hrW1Ndq2bQvg5UEaOTk52LlzJ3x8fETLlpmZiXXr1uHXX3/Fo0eP8MUXXyA2Nla0PPRm/v7+8Pf3x8CBA8WOAuBlB9e9e/cQHR0NExMTpKenQ1NTE/7+/nB3d8eSJUvEjkivefz4Mby9vXH27Fl89tlnAF4eDtehQwfExMTAyMhI3ID0QajomnyVTCaDQqEQ7SCo69evw9vbG/n5+Xj+/DmuXr0KKysrjBkzBiUlJYLP7i4uLsbo0aOxYcMGAFDmCQkJQYMGDTB58mRB8wCAl5eXyucaGhqoU6cOOnbsiODgYFSrxj48+rixaEpEREQkUX/88QdWrVqFK1euQKFQwM7ODsOHDxe00/Rt5HI59u7di/Xr17NoKlFr1qzB9OnTERgYiFatWqltpxa6U7CwsBA+Pj64fPkynjx5ggYNGqCgoADt2rXDgQMHuN1bohQKBY4cOYK0tDTo6OjA0dER7u7uYseiD8j169ffet3c3FygJP/H398f+vr6WLduHWrVqqWcsZyUlITBgwcjKytL0DyhoaFISUnBkiVL4O3tjfT0dFhZWSE2NhbTpk3D+fPnBc1DRCyaEhERERF9tCrr7qogVncXAMTHxyM1NVV5MFXnzp1FyUFEn67atWsjJSUFTZo0UTmYLi8vD3Z2diguLhY0j7m5ObZt24a2bduq5MnOzkbLli0rHW0ilLt37yIzMxMymQy2trYwNTUVLQuRkNhLTURERCRRjx49wunTp3H37l2Ul5erXOvfv79IqehD8vrXjVR07NgRHTt2FDsGvaO4uDjExcVV+rOo4sRxotfFxsaia9eu0NLS+svdCGLMxy0vL6/0wdEff/whyqzue/fuVVqMLCoqgkwmEzwP8HI8x6hRo7Blyxbl976mpiYCAgKwcuVKGBoaipKLSCjsNCUiIiKSoL179yIwMBBFRUXQ19dXecMkk8nw8OFDEdMRvb/y8nJERkYiJiYGeXl5kMlksLS0xDfffIN+/fqJVhSgt5sxYwZmzpyJ1q1bo379+mp/T7t27RIpGUmdhoYGCgoKYGpqKsmu94CAABgaGmLt2rXQ19dHeno66tSpg+7du6NRo0aIiIgQNI+Hhwe++eYbjB49WpnH0tISo0aNQnZ2Ng4ePChoHgDo1asXLly4gOXLl6Ndu3aQyWQ4ceIEQkND4ejoiOjoaMEzEQmJRVMiIiIiCbK1tYWPjw/mzp0r2im+9OGKj4/HqFGjcOrUKRgYGKhce/z4MVxdXbF69WrB5lIqFAp069YNBw4cQIsWLdC0aVMoFApkZGTg4sWL8PPzw+7duwXJQu+nfv36mD9/Pvr16yd2FKL/qVu3bsHLywuamprIyspC69atkZWVhdq1a+PYsWOCb0E/ceIEvL29ERgYiMjISAwbNgyXL1/GyZMnkZSUhFatWgmaBwD09PRw6NAhtG/fXmX9+PHj8Pb2RlFRkeCZiITE7flEREREEnTz5k2EhISwYEp/y5IlSzBkyBC1gikAGBoaYtiwYVi8eLFgRdPIyEgcO3YMcXFxaqcxx8fHw9/fH1FRURw7IUEvXryAq6ur2DGI/ucaNGiACxcuYMuWLcoZy4MGDUJgYCB0dHQEz+Pq6oqUlBQsWLAA1tbWOHz4MFq2bImTJ0/CwcFB8DwAUKtWrUq34BsaGsLY2FiERETCYqcpERERkQT17NkTvXv3Rq9evcSOQh8gc3NzHDx4EM2aNav0+pUrV9ClSxfk5+cLkqdLly7o2LEjJk+eXOn1uXPnIikpCYcOHRIkD727SZMmoWbNmpgyZYrYUYj+p4qLi/lg8i+sXbsW27dvR1RUFOrXrw8AKCgowIABA9CzZ08MGzZM5IREVYudpkREREQS5OvriwkTJuD333+Hg4MDtLS0VK6LcWgGfTju3Lmj9jXzqmrVquHevXuC5UlPT8f8+fPfeL1r165YtmyZYHno3ZWUlGDt2rU4evQoHB0d1b6uFi1aJFIyov+Oqakp/P390a9fP3zxxRdvnbsqBC8vL/Tt2xfffPONZA5YWr16NbKzs2Fubo5GjRoBAPLz81G9enXcu3cPa9asUb42NTVVrJhEVYZFUyIiIiIJGjJkCABg5syZatfEOjSDPhwNGzbExYsXYWNjU+n19PR0ZdeQEB4+fIi6deu+8XrdunXx559/CpaH3l16ejqcnJwAAJcuXVK5xsO76EMWFRWFLVu2oEePHjAwMEBAQAD69u2LNm3aiJLHwcEB//znPzFq1Cj4+PigX79+8PHxgba2tih5AMDf31+0exNJAbfnExERERF9ZEaPHo3ExEScOXMGNWrUULn27NkzuLi4wMvLS7DuTk1NTRQUFKBOnTqVXr9z5w4aNGjAhwFEJLgnT55gx44d2LJlCxISEmBpaYm+ffti6tSpgmcpLy/H0aNHsXnzZuzatQuampr45ptvEBgYCA8PD8HzEH3qWDQlIiIiIvrI3LlzBy1btoSmpiZGjRqFJk2aQCaTISMjAytXroRcLkdqaupbuz//lzQ0NNC1a1dUr1690uvPnz/HwYMHWTQl+kilpqZCS0tLeaDRnj17EBERATs7O0yfPl3UbspX/f777wgMDER6erroP49KSkqwd+9ezJkzBxcvXhQ9D9GniNvziYiIiCSqqKgISUlJyM/Px4sXL1SuhYSEiJSKPgR169bFiRMnMGLECPzwww+o6JOQyWT48ssvsWrVKsEKpgAwYMCAv3xN//79BUhC76pnz57v9LqYmJgqTkIfg2HDhmHy5MlwcHDAtWvX0Lt3b/To0QPbt29HcXExlixZIlq2kpISxMbGYvPmzTh48CBMTU0xfvx40fIALw9b2rp1KzZu3Ij09HTRRgYQferYaUpEREQkQefPn4ePjw+Ki4tRVFQEExMT3L9/H7q6ujA1NcW1a9fEjkgfiD///BPZ2dlQKBRo3LgxjI2NxY5EH4CgoKB3el1EREQVJ6GPgaGhIVJTU2FtbY2ffvoJ8fHxOHToEFJSUtC7d2/cuHFD8EyHDx/Gpk2bsHv3bklsgy8sLMTOnTuxefNmJCYmwsrKCn369EFgYOAb51MTUdVi0ZSIiIhIgjw9PWFra4vVq1fDyMgIaWlp0NLSQt++fREaGvrOXWBERERiMzAwwLlz59C4cWN88cUX+OqrrxAaGor8/Hw0adIEz549EzyTrq4ufH19ERgYCF9fX2hpaQme4VU6OjowNjZGr169EBgYyO5SIglg0ZSIiIhIgoyMjPDbb7+hSZMmMDIywsmTJ9GsWTP89ttvGDBgAK5cuSJ2RCIionfSsWNHmJmZoXPnzhg0aBB+//132NjYICkpCQMGDEBeXp7gmQoLC2FgYCD4fd/k8OHD6Ny5MzQ0NMSOQkT/H2eaEhEREUmQlpYWZDIZgJfzKfPz89GsWTMYGhoiPz9f5HRERETvbsmSJQgMDMTu3bvx448/Kreb79ixA66urqJkMjAwQHl5ObKzs3H37l2Ul5erXHd3dxc0T5cuXQS937v6448/EBsbW+l89UWLFomUikgYLJoSERERSZCzszPOnj0LW1tbeHl5YerUqbh//z5+/fVX5enDREREHwJHR0dcvHhRbf3nn3+GpqamCImAU6dOoU+fPrh+/Tpe34Ark8kEP63+zp07GD9+POLi4nD37l21TELnAYC4uDj4+fnB0tISmZmZaN68OfLy8qBQKNCyZUvB8xAJjdvziYiIiCTo7NmzePLkCby8vHDv3j0MGDAAycnJsLGxQUREBFq0aCF2RCIiog+Wk5MTbG1tMWPGDNSvX1+5u6OCoaGhoHm6du2K/Px8jBo1qtI83bt3FzQPALi4uMDb2xszZ86Evr4+0tLSYGpqisDAQHh7e2PEiBGCZyISEoumRERERERERPQ/Z2lpqVL8u3btmohpVOnp6SEtLU0yJ9Pr6+vj+PHjcHJyEjuKkr6+Pi5cuABra2sYGxsjOTkZ9vb2SEtLQ/fu3UWZRUskJG7PJyIiIiIiIqL/ucjISLEjvNHnn3+O7OxsyRRNzczM1Lbki01PTw/Pnz8HADRo0AA5OTmwt7cHANy/f1/MaESCYNGUiIiIiIiIiP7nPDw8xI7wRqNHj0ZYWBgKCgrg4OAALS0tleuOjo6C5lmyZAkmT56MNWvWwMLCQtB7v0nbtm2RkpICOzs7+Pr6IiwsDBcvXkRMTAzatm0rdjyiKsft+URERERERERUZTw9PREcHIxvv/0WOjo6YscBAGhoaKityWQyKBQKUQ6CMjY2RnFxMcrKyqCrq6tWxH348KGgeYCX4xSePn0KR0dHFBcXY/z48cr56osXL4a5ubngmYiExKIpEREREREREVWZsLAwbNq0Cc+ePUOvXr0waNAg0TsVr1+//tbrQhcEN2zY8NbrAwYMECgJEVVg0ZSIiIjoA/Ho0SMYGRmJHYOIiOi9yeVy7Nu3DxEREThw4ABsbGwQHByMfv36oW7dumLHIyJSw6IpERERkQT99NNPsLCwQEBAAACgV69e2LlzJ+rVq4cDBw6gRYsWIickIiL6e+7du4c1a9Zgzpw5kMvl8PHxQUhICDp27Fil942NjUXXrl2hpaWF2NjYt77Wz8+vSrMAQGFhIQwMDJQfv03F64hIOCyaEhEREUmQlZUVNm7cCFdXVxw5cgS9evXCtm3bEB0djfz8fBw+fFjsiERERO/t9OnTiIiIwJYtW2BoaIiBAwfi9u3b2LRpE0aMGIEFCxZU2b01NDRQUFAAU1PTSmeaVhBqpqmmpiZu376tzCOTydReI9aMVSICqokdgIiIiIjU3b59G2ZmZgCAffv2oVevXujSpQssLCzw+eefi5yOiIjo3d29exe//vorIiIikJWVhW7dumHr1q348ssvlYXCXr16wd/fv0qLpuXl5ZV+LJb4+HiYmJgAABISEkROQ0SvY9GUiIiISIKMjY1x48YNmJmZ4eDBg5g9ezaAlx0n7DYhIqIPyWeffQZra2sEBwdj4MCBqFOnjtprXFxc0KZNGxHSicfDw6PSj4lIGlg0JSIiIpKgnj17ok+fPmjcuDEePHiArl27AgAuXLgAGxsbkdMRERG9u7i4OHTo0OGtrzEwMGC3pcTI5XJERkYiLi4Od+/eVevOjY+PFykZkTBYNCUiIiKSoMWLF8PCwgI3btzA/PnzUbNmTQAvt+1///33IqcjIiJ6d39VMCVpCg0NRWRkJHx9fdG8efNKZ64Sfcx4EBQRERERERERVZk7d+5g/Pjxyo7F18sQHDsjTbVr10ZUVBR8fHzEjkIkCnaaEhEREUnU1atXkZiYWOmWuKlTp4qUioiI6P0MHDgQ+fn5mDJlCurXr8+OxQ+EtrY2RwLRJ42dpkREREQS9Msvv2DEiBGoXbs26tWrp/IGUyaTITU1VcR0RERE705fXx/Hjx+Hk5OT2FGUUlNToaWlBQcHBwDAnj17EBERATs7O0yfPh3a2tqC5nn27BkUCgV0dXUBANevX8euXbtgZ2eHLl26CJqlwsKFC3Ht2jWsWLGChW76JLFoSkRERCRB5ubm+P777zFp0iSxoxAREf1X7OzssGnTJjg7O4sdRalNmzaYPHkyvv76a1y7dg329vbo0aMHzpw5A19fXyxZskTQPF26dEHPnj0xfPhwPHr0CE2bNoWWlhbu37+PRYsWYcSIEYLk6Nmzp8rn8fHxMDExgb29PbS0tFSuxcTECJKJSCwaYgcgIiIiInV//vknvv32W7FjEBER/deWLFmCyZMnIy8vT+woSlevXlV2vm7fvh3u7u7YvHkzIiMjsXPnTsHzpKamKg/M2rFjB+rWrYvr168jKioKy5YtEyyHoaGhyn89evSAh4cHateurXaN6GPHmaZEREREEvTtt9/i8OHDGD58uNhRiIiI/isBAQEoLi6GtbU1dHV11ToWHz58KHgmhUKhnBd+9OhRfPXVVwAAMzMz3L9/X/A8xcXF0NfXBwAcPnwYPXv2hIaGBtq2bYvr168LliMiIkKwexFJHYumRERERBJkY2ODKVOm4NSpU3BwcFB7gxkSEiJSMiIiovcj9Fb3d9G6dWvMnj0bnTt3RlJSElavXg0AyM3NRd26dQXPY2Njg927d6NHjx44dOgQxo4dCwC4e/cuDAwMBM8DAB07dkRMTAyMjIxU1gsLC+Hv74/4+HhRchEJhTNNiYiIiCTI0tLyjddkMhmuXbsmYBoiIqKPS3p6OgIDA5Gfn49x48Zh2rRpAIDRo0fjwYMH2Lx5s6B5duzYgT59+kAul6NTp044fPgwAOBf//oXjh07hv/85z+C5gEADQ0NFBQUwNTUVGX97t27aNiwIUpLSwXPRCQkFk2JiIiIiIiISBDPnj1TK7aJ1UlZmZKSEmhqaqrt8BBCQUEBbt++jRYtWkBD4+URNKdPn4aBgQGaNm0qWI709HQAgJOTk/IgqApyuRwHDx7EmjVrJDWjlqgqsGhKREREJHEV/1yTyWQiJyEiInp/RUVFmDRpEqKjo/HgwQO163K5XIRU9CYaGhrKf3NUVjLS0dHB8uXLERwcLHQ0IkFxpikRERGRREVFReHnn39GVlYWAMDW1hYTJkxAv379RE5GRET07iZOnIiEhASsWrUK/fv3x8qVK3Hz5k2sWbMG8+bNEzSLpaWlykNIscfdvF54XL9+vUhJ/k9ubi4UCgWsrKxw+vRp1KlTR3lNW1sbpqam0NTUFDEhkTBYNCUiIiKSoEWLFmHKlCkYNWoU3NzcoFAokJKSguHDh+P+/fvKAyKIiIikbu/evYiKioKnpyeCg4PRoUMH2NjYwNzcHJs2bUJgYKBgWSIjIwW717swNzcXO4Kaikzl5eUiJyESF7fnExEREUmQpaUlZsyYgf79+6usb9iwAdOnT0dubq5IyYiIiN5PzZo1cfnyZZibm+Ozzz5DTEwMXFxckJubCwcHBzx9+lTsiPT/xcbGomvXrtDS0kJsbOxbX+vn5ydQKiJxsNOUiIiISIJu374NV1dXtXVXV1fcvn1bhERERER/j5WVFfLy8mBubg47OztER0fDxcUFe/fuhZGRkSiZKrpev/32W+jo6IiS4VXTp09HUFCQ6J2n/v7+KCgogKmpKfz9/d/4OplMxlm09NHTEDsAEREREamzsbFBdHS02vq2bdvQuHFjERIRERH9PUFBQUhLSwMA/PDDD1i1ahWqV6+OsWPHYsKECaJkatWqFSZOnIh69ephyJAhOHXqlCg5KuzduxfW1tbo1KkTNm/ejJKSElFylJeXw9TUVPnxm/5jwZQ+BdyeT0RERCRBO3fuREBAADp37gw3NzfIZDIkJycjLi4O0dHR6NGjh9gRiYiI/pbr16/j3LlzsLa2RosWLUTLIZfLsW/fPkRERODAgQOwsbFBcHAw+vXrh7p16wqeJz09HREREdi8eTNevHiB3r17Izg4GG3atBE8CwAUFxdDV1dXlHsTSQGLpkREREQSde7cOSxevBgZGRlQKBSws7NDWFgYnJ2dxY5GRET0Ubl37x7WrFmDOXPmQC6Xw8fHByEhIejYsaPgWcrKyrB3715ERETg4MGDaNKkCQYPHoyBAwfC0NBQsBza2tpo3bo1PD094eHhgfbt20NPT0+w+xOJjUVTIiIiIiIiIqpScXFxygeBMpkMTZs2xZgxY9C5c2exo+H06dOIiIjAli1bYGhoiIEDB+L27dvYtGkTRowYgQULFgia58WLF9i1axfWr1+P+Ph4uLq64s6dO7h16xZ++eUXBAQECJLj5MmTSEpKQmJiIk6cOIGSkhK0bNlSWUTt2rWrIDmIxMKiKREREZFEFBYWwsDAQPnx21S8joiISOpWrFiBsWPH4ptvvkG7du0AAKdOncKOHTuwaNEijBo1SvBMd+/exa+//oqIiAhkZWWhW7duGDx4ML788kvIZDIAwNGjR+Hv74+nT58KkuncuXPK4m316tXRv39/DB48GDY2NgCAhQsXYv78+bhz544geV4ll8tx5swZhIeHY9OmTZxrSp8EFk2JiIiIJEJTUxO3b9+GqakpNDQ0lG/aXqVQKHhiLRERfVAaNmyIH374Qa04unLlSsyZMwe3bt0SPJO2tjasra0RHByMgQMHok6dOmqvKSwsRPfu3ZGQkFDleRwdHZGRkYEuXbpgyJAh6NatGzQ1NVVec+/ePdStWxfl5eVVnqfClStXkJiYqOw4LS0thbu7Ozw8PBAaGipYDiIxsGhKREREJBFJSUlwc3NDtWrVkJSU9NbXenh4CJSKiIjov6Ovr4/z588rOyYrZGVlwdnZWbBOzlcdP34cHTp0EPy+bzJr1iwEBwejYcOGYkdRqlevHkpLS9GxY0d4enrC3d0dDg4OYsciEgyLpkREREQSlJ+fDzMzM7VuU4VCgRs3bqBRo0YiJSMiIno/gYGBcHJywoQJE1TWFyxYgHPnzmHLli0iJaO3cXJyQkZGBpycnODp6QlPT0906NABNWvWFDsakSBYNCUiIiKSoFe36r/qwYMHMDU15fZ8IiL6YMyePRsLFiyAm5ubykzTlJQUhIWFqczpDgkJESTTnTt3MH78eMTFxeHu3bt4vTQi9O9ZuVyOyMhIZZ7Xt+DHx8cLmqfCo0ePcOzYMSQlJSEpKQmXL1+Go6MjvLy8MG/ePFEyEQmFRVMiIiIiCdLQ0MCdO3fUZqxdv34ddnZ2KCoqEikZERHR+7G0tHyn18lkMly7dq2K07zUtWtX5OfnY9SoUahfv77azo7u3bsLkqPCqFGjEBkZCV9f30rzLF68WNA8r3v48CESExOxZ88ebN68mQdB0SeBRVMiIiIiCRk3bhwAYOnSpRgyZAh0dXWV1+RyOX777TdoamoiJSVFrIhEREQfPH19fRw/fhxOTk5iRwEA1K5dG1FRUfDx8RE7itKuXbuQmJiIxMREXL58GbVq1UKHDh3g6ekJLy8v2Nvbix2RqEpVEzsAEREREf2f8+fPA3g5u/TixYvQ1tZWXtPW1kaLFi0wfvx4seIRERF9FMzMzNS25ItJW1tb7aAssQ0bNgzu7u4YMmQIPD090bx5c7EjEQmKnaZEREREEhQUFISlS5eqzHkjIiL6ECkUCuzYsQMJCQmVzuuMiYkRPNPhw4excOFCrFmzBhYWFoLf/3ULFy7EtWvXsGLFCrWt+UQkDhZNiYiIiIiIiKjKhISEYO3atfDy8kLdunXVioIRERGCZzI2NkZxcTHKysqgq6sLLS0tlesPHz4UNE+PHj2QkJAAExMT2Nvbq+URo7BM9Knj9nwiIiIiiTpz5gy2b9+O/Px8vHjxQuUa3zwREdGHYuPGjYiJiZHUvM4lS5aIHUGFkZERevToIXYMInoFi6ZEREREErR161b0798fXbp0wZEjR9ClSxdkZWWhoKCAb6qIiOiDYmhoCCsrK7FjqBgwYIDYEVSI0W1LRG/H7flEREREEuTo6Ihhw4Zh5MiR0NfXR1paGiwtLTFs2DDUr18fM2bMEDsiERHRO9mwYQMOHjyI9evXQ0dHR+w4ap49e4bS0lKVNc4UJyIWTYmIiIgkSE9PD5cvX4aFhQVq166NhIQEODg4ICMjAx07dsTt27fFjkhERPROiouL0bNnT6SkpMDCwkJtXmdqaqrgmYqKijBp0iRER0fjwYMHatflcrngmXbs2IHo6OhKx/KI8WdE9Knj9nwiIiIiCTIxMcGTJ08AAA0bNsSlS5fg4OCAR48eobi4WOR0RERE727gwIE4d+4c+vbtW+lBUGKYOHEiEhISsGrVKvTv3x8rV67EzZs3sWbNGsybN0/wPMuWLcOPP/6IAQMGYM+ePQgKCkJOTg7OnDmDkSNHCp4HAEpKSrB8+XIkJCTg7t27KC8vV7nOQi597Fg0JSIiIpKgDh064MiRI3BwcECvXr0QGhqK+Ph4HDlyBJ06dRI7HhER0Tvbv38/Dh06hPbt24sdRWnv3r2IioqCp6cngoOD0aFDB9jY2MDc3BybNm1CYGCgoHlWrVqFtWvX4rvvvsOGDRswceJEWFlZYerUqXj48KGgWSoEBwfjyJEj+Oabb+Di4iKJYjeRkFg0JSIiIpKgFStWoKSkBADwww8/QEtLC8nJyejZsyemTJkicjoiIqJ3Z2ZmJrkZoQ8fPoSlpSWAl/NLKwqT7du3x4gRIwTPk5+fD1dXVwCAjo6OcrdJv3790LZtW6xYsULwTPv378eBAwfg5uYm+L2JpEBD7ABEREREpKqsrAx79+6FhsbLf6ppaGhg4sSJiI2NxaJFi2BsbCxyQiIione3cOFCTJw4EXl5eWJHUbKyslLmsbOzQ3R0NICXHahGRkaC56lXr55ytqq5uTlOnToFAMjNzYVYR9E0bNgQ+vr6otybSAp4EBQRERGRBOnq6iIjIwPm5uZiRyEiIvqvGBsbo7i4GGVlZdDV1VU7CEqM7eeLFy+GpqYmQkJCkJCQAF9fX8jlcpSVlWHRokUIDQ0VNM/gwYNhZmaGadOmITw8HOPGjYObmxvOnj2Lnj17Yt26dYLmAYD//Oc/WLZsGcLDw/nvEfoksWhKREREJEFeXl4IDQ2Fv7+/2FGIiIj+Kxs2bHjr9QEDBgiU5M2uX7+Oc+fOwdraGi1atBD8/uXl5SgvL0e1ai+nKEZHRyM5ORk2NjYYPnw4tLW1Bc9079499OrVC8eOHZNMsZtISCyaEhEREUnQ9u3bMXnyZIwdOxatWrWCnp6eynVHR0eRkhEREdGnoHPnzsjPz8egQYNQt25dtYOgpFDsJqpKLJoSERERSVDFPNNXyWQyKBQKyGQyyOVyEVIRERH9PXK5HLt370ZGRgZkMhns7Ozg5+cHTU1N0TLFxcVh8eLFykxNmzbFmDFj0LlzZ1Hy/Pnnn1i3bp0yT7NmzRAUFAQTExNR8ujq6uLkyZOidN4SSQGLpkREREQSdP369bde52wxIiL6UGRnZ8PHxwc3b95EkyZNoFAocPXqVZiZmWH//v2wtrYWPNOKFSswduxYfPPNN2jXrh0A4NSpU9ixYwcWLVqEUaNGCZonKSkJ3bt3h4GBAVq3bg0AOHfuHB49eoTY2Fh4eHgImgcAWrZsiVWrVqFt27aC35tIClg0JSIiIiIiIqIq4+PjA4VCgU2bNim7Jh88eIC+fftCQ0MD+/fvFzxTw4YN8cMPP6gVR1euXIk5c+bg1q1bguZp3rw5XF1dsXr1amX3rVwux/fff4+UlBRcunRJ0DwAcPjwYcyYMQNz5syBg4OD2kxTAwMDwTMRCYlFUyIiIiKJ+vXXXxEeHo7c3FycPHkS5ubmWLJkCSwtLdG9e3ex4xEREb0TPT09nDp1Cg4ODirraWlpcHNzw9OnTwXPpK+vj/Pnz8PGxkZlPSsrC87OzoJn0tHRwYULF9CkSROV9czMTDg5OeHZs2eC5gH+b1TQ67NMOSqIPhXVxA5AREREROpWr16NqVOnYsyYMZgzZ47yjYmRkRGWLFnCoikREX0wqlevjidPnqitP336VJRT4QHAz88Pu3btwoQJE1TW9+zZg27dugmep2XLlsjIyFArmmZkZMDJyUnwPACQkJAgyn2JpIKdpkREREQSZGdnh7lz58Lf3x/6+vpIS0uDlZUVLl26BE9PT9y/f1/siERERO+kf//+SE1Nxbp16+Di4gIA+O233zBkyBC0atUKkZGRgmeaPXs2FixYADc3N5WZpikpKQgLC1PZeh4SElLlebZt24aJEydi9OjRyhmip06dwsqVKzFv3jw0a9ZM+VpHR8cqz0NELJoSERERSZKOjg6uXLkCc3NzlaJpVlYWHB0dRdmmR0RE9Hc8evQIAwYMwN69e5VzMcvKyuDn54fIyEgYGhoKnsnS0vKdXieTyXDt2rUqTvN/W+HflkOMbfHHjx/HmjVrcO3aNWzfvh0NGzbEr7/+CktLS7Rv316wHERi4PZ8IiIiIgmytLTEhQsXYG5urrL+n//8B3Z2diKlIiIien9GRkbYs2cPsrOzkZGRAYVCATs7O7V5okLKzc0V7d6VkVoeANi5cyf69euHwMBApKam4vnz5wCAJ0+eYO7cuThw4IDICYmqFoumRERERBI0YcIEjBw5EiUlJVAoFDh9+jS2bNmCf/3rX/j3v/8tdjwiIqL3ZmNjI2qhVMpef0gqBbNnz0Z4eDj69++PrVu3KtddXV0xc+ZMEZMRCYNFUyIiIiIJCgoKQllZGSZOnIji4mL06dMHDRs2xNKlS9G7d2+x4xEREb2zb775Bq1bt8bkyZNV1n/++WecPn0a27dvFzyTQqHAjh07kJCQgLt376K8vFzlekxMjOCZbt68iZSUlErzCDFX9XWZmZlwd3dXWzcwMMCjR48Ez0MkNBZNiYiIiCRqyJAhGDJkCO7fv4/y8nKYmpqKHYmIiOi9JSUlYdq0aWrr3t7eWLBggQiJgNDQUKxduxZeXl6oW7cuZDKZKDkqREREYPjw4dDW1katWrVU8shkMlGKpvXr10d2djYsLCxU1pOTk2FlZSV4HiKhsWhKREREJGF3795FZmYmZDIZZDIZ6tSpI3YkIiKi9/L06VNoa2urrWtpaaGwsFCERMDGjRsRExMDHx8fUe7/uqlTp2Lq1Kn44Ycf/vJQKKEMGzYMoaGhWL9+PWQyGW7duoWTJ09i/PjxmDp1qtjxiKqcNL4TiYiIiEhFYWEh+vXrhwYNGsDDwwPu7u5o0KAB+vbti8ePH4sdj4iI6J01b94c27ZtU1vfunWraIcbGhoaSqpbsri4GL1795ZMwRQAJk6cCH9/f3h5eeHp06dwd3fH4MGDMWzYMIwaNUrseERVTqZQKBRihyAiIiIiVb169cKFCxewfPlytGvXDjKZDCdOnEBoaCgcHR0RHR0tdkQiIqJ3Ehsbi6+//hp9+vRBx44dAQBxcXHYsmULtm/fDn9/f8EzbdiwAQcPHsT69euho6Mj+P1fN3HiRJiYmKjNfZWC4uJi/P777ygvL4ednR1q1qwpdiQiQbBoSkRERCRBenp6OHToENq3b6+yfvz4cXh7e6OoqEikZERERO9v//79mDt3Li5cuAAdHR04Ojpi2rRp8PDwECVPcXExevbsiZSUFFhYWEBLS0vlempqqqB55HI5vvrqKzx79gwODg5qeRYtWiRoHiLiTFMiIiIiSapVqxYMDQ3V1g0NDWFsbCxCIiIior/P19cXvr6+YsdQGjhwIM6dO4e+fftK4iCouXPn4tChQ2jSpAkAqB0EJZSePXu+82tjYmKqMAmR+Fg0JSIiIpKgf/7znxg3bhyioqJQv359AEBBQQEmTJiAKVOmiJyOiIjow7Z///5Kd3SIZdGiRVi/fj0GDhwoao7KHtgSfaq4PZ+IiIhIgpydnZGdnY3nz5+jUaNGAID8/HxUr14djRs3Vnmt0FsIiYiIPnRNmzZFdHQ0HB0dxY4CAKhXrx6OHz+u9jueiMTDTlMiIiIiCRLjUAwiIqJPxcKFCzFx4kSEh4fDwsJC7DgIDQ3F8uXLsWzZMrGjqLl79y4yMzMhk8lga2sLU1NTsSMRCYKdpkRERERERET0STE2NkZxcTHKysqgq6urdvDSw4cPBc3To0cPxMfHo1atWrC3t1fLI8b80MLCQowcORJbt26FXC4HAGhqaiIgIAArV67kVn766LHTlIiIiEiCnj17hiNHjuDq1avKzo7OnTtDR0dH7GhEREQfvCVLlogdQYWRkdF7HcIkhMGDB+PChQvYt28f2rVrB5lMhhMnTiA0NBRDhgxBdHS02BGJqhQ7TYmIiIgkJjY2FoMHD8b9+/dV1mvXro1169ahW7duIiUjIiL6e/744w/ExsYiPz8fL168ULm2aNEikVLR2+jp6VV6WNbx48fh7e2NoqIikZIRCYOdpkREREQScuLECXzzzTfw8/NDWFgYmjVrBgD4/fffsXDhQnzzzTdITExEu3btRE5KRET0buLi4uDn5wdLS0tkZmaiefPmyMvLg0KhQMuWLUXLJZfLsXv3bmRkZEAmk8HOzg5+fn7Q1NQULdO9e/dU5ofWqVNHtCy1atWqdAu+oaEhjI2NRUhEJCx2mhIRERFJiI+PD8zMzLBmzZpKrw8bNgw3btzAgQMHBE5GRET097i4uMDb2xszZ86Evr4+0tLSYGpqisDAQHh7e2PEiBGCZ8rOzoaPjw9u3ryJJk2aQKFQ4OrVqzAzM8P+/fthbW0taJ6ioiKMHj0aUVFRKC8vB/Byfmj//v2xfPly6OrqCpoHANauXYvt27cjKioK9evXBwAUFBRgwIAB6NmzJ4YNGyZ4JiIhsWhKREREJCHGxsY4duwYHBwcKr2enp4ODw8P/PnnnwInIyIi+nv09fVx4cIFWFtbw9jYGMnJybC3t0daWhq6d++OvLw8wTP5+PhAoVBg06ZNMDExAQA8ePAAffv2hYaGBvbv3y9onmHDhuHo0aNYsWIF3NzcAADJyckICQnBF198gdWrVwuSw9nZGTKZTPl5VlYWnj9/jkaNGgEA8vPzUb16dTRu3BipqamCZCISC7fnExEREUlISUkJDAwM3njd0NAQz58/FzARERHRf0dPT0/5u6tBgwbIycmBvb09AKjN7xZKUlISTp06pSyYAi+3o8+bN09ZtBTSzp07sWPHDnh6eirXfHx8oKOjg169eglWNPX39xfkPkQfAhZNiYiIiCTE1tYW8fHxCAoKqvR6XFwcbGxsBE5FRET097Vt2xYpKSmws7ODr68vwsLCcPHiRcTExKBt27aiZKpevTqePHmitv706VNoa2sLnqe4uBh169ZVWzc1NUVxcbFgOaZNmybYvYikTkPsAERERET0fwYOHIjx48dXOrN0//79mDhx4hsLqkRERFK0aNEifP755wCA6dOn44svvsC2bdtgbm6OdevWiZLpq6++wtChQ/Hbb79BoVBAoVDg1KlTGD58OPz8/ATP065dO0ybNg0lJSXKtWfPnmHGjBmiH/547tw5bNy4EZs2bcL58+dFzUIkJM40JSIiIpKQ8vJyBAQEYOfOnWjSpAmaNWsGAPj999+RlZUFf39/bN++HRoafPZNRET0dz169AgDBgzA3r17oaWlBQAoKyuDn58fIiMjKz01vipdunQJ3t7eKCkpQYsWLSCTyXDhwgXUqFEDhw4dUo4zENLdu3fRu3dvJCYmwsjICAqFAo8fP4aXlxe2bt2KOnXqCJ6JSEgsmhIRERFJ0LZt27BlyxZcvXoVwMtt+71790bv3r1FTkZERPTxyM7ORkZGBhQKBezs7EQdgfPs2TNs3LgRV65cUeYJDAyEjo6OKHkCAgKQk5ODX3/9VeUh7oABA2BjY4MtW7aIkotIKCyaEhERERERERGRCkNDQxw9ehRt2rRRWT99+jS6dOmCR48eiROMSCDc10VEREREREREn5RvvvkG8+bNU1v/+eef8e233wqe51//+hfWr1+vtr5+/Xr89NNPgucBXo4Mqhhd8CotLS2Ul5eLkIhIWCyaEhEREREREdEnJSkpCb6+vmrr3t7eOHbsmOB51qxZg6ZNm6qt29vbIzw8XPA8ANCxY0eEhobi1q1byrWbN29i7Nix6NSpkyiZiITEoikRERERERERfVKePn0KbW1ttXUtLS0UFhYKnqegoAD169dXW69Tpw5u374teB4AWLFiBZ48eQILCwtYW1vDxsYGlpaWePLkCZYvXy5KJiIhVRM7ABERERERERF9vORyOSIjIxEXF4e7d++qbe2Oj48XPFPz5s2xbds2TJ06VWV969atsLOzEzyPmZkZUlJSYGlpqbKekpKCBg0aCJ6nIlNqaiqOHDmicjhV586dRclDJDQWTYmIiIgkKDg4GEuXLoW+vr7KelFREUaPHl3p3DMiIiIpCg0NRWRkJHx9fdG8eXPIZDKxI2HKlCn4+uuvkZOTg44dOwIA4uLisGXLFmzfvl3wPIMHD8aYMWNQWlqqkmfixIkICwsTPE9ZWRlq1KiBCxcu4IsvvsAXX3wheAYisckUCoVC7BBEREREpEpTUxO3b9+Gqampyvr9+/dRr149lJWViZSMiIjo/dSuXRtRUVHw8fERO4qK/fv3Y+7cubhw4QJ0dHTg6OiIadOmwcPDQ/AsCoUCkydPxrJly/DixQsAQI0aNTBp0iS1blihWFtbIyYmBi1atBDl/kRiY9GUiIiISEIKCwuhUChgbGyMrKws1KlTR3lNLpdj7969mDx5ssqhDERERFLWoEEDJCYmwtbWVuwokvf06VNkZGRAR0cHjRs3RvXq1UXLEhERge3bt2Pjxo0wMTERLQeRWFg0JSIiIpIQDQ2Nt25blMlkmDFjBn788UcBUxEREf19CxcuxLVr17BixQpJbM2nd+Ps7Izs7GyUlpbC3Nwcenp6KtdTU1NFSkYkDM40JSIiIpKQhIQEKBQKdOzYETt37lTp7NDW1oa5ubloB0IQERG9q549e6p8Hh8fj//85z+wt7eHlpaWyrWYmBgho9E76t69O4vc9EljpykRERGRBF2/fh1mZmbQ0NAQOwoREdF7CwoKeufXRkREVGESIqK/h0VTIiIiIol69OgRTp8+jbt376K8vFzlWv/+/UVKRURERB+z4uJiTJgwAbt370ZpaSk6d+6MZcuWoXbt2mJHIxIUi6ZEREREErR3714EBgaiqKgI+vr6KtvjZDIZHj58KGI6IiKid9exY0fExMTAyMhIZb2wsBD+/v6Ij48XJxhVasKECVi1ahUCAwNRo0YNbNmyBZ6enti+fbvY0YgExaIpERERkQTZ2trCx8cHc+fOha6urthxiIiI/jYNDQ0UFBTA1NRUZf3u3bto2LAhSktLRcn1xx9/IDY2Fvn5+Xjx4oXKtUWLFgmaJTY2ttJ1mUyGGjVqwMbGBpaWloJksba2xpw5c9C7d28AwOnTp+Hm5oaSkhJoamoKkoFICngQFBEREZEE3bx5EyEhISyYEhHRBys9PV358e+//46CggLl53K5HAcPHkTDhg3FiIa4uDj4+fnB0tISmZmZaN68OfLy8qBQKNCyZUvB8/j7+0Mmk+H1vraKNZlMhvbt22P37t0wNjau0iw3btxAhw4dlJ+7uLigWrVquHXrFszMzKr03kRSwpMFiIiIiCToyy+/xNmzZ8WOQURE9Lc5OTnB2dkZMpkMHTt2hJOTk/K/Vq1aYfbs2Zg6daoo2X744QeEhYXh0qVLqFGjBnbu3IkbN27Aw8MD3377reB5jhw5gjZt2uDIkSN4/PgxHj9+jCNHjsDFxQX79u3DsWPH8ODBA4wfP77Ks8jlcmhra6usVatWDWVlZVV+byIp4fZ8IiIiIglat24dZs6ciaCgIDg4OEBLS0vlup+fn0jJiIiI3s3169ehUChgZWWF06dPo06dOspr2traMDU1FW27t76+Pi5cuABra2sYGxsjOTkZ9vb2SEtLQ/fu3ZGXlydonubNm2Pt2rVwdXVVWU9JScHQoUNx+fJlHD16FMHBwcjPz6/SLBoaGujatSuqV6+uXNu7dy86duwIPT095VpMTEyV5iASG7fnExEREUnQkCFDAAAzZ85UuyaTySCXy4WORERE9F7Mzc0BAOXl5SInUaenp4fnz58DABo0aICcnBzY29sDAO7fvy94npycHBgYGKitGxgY4Nq1awCAxo0bC5JtwIABamt9+/at8vsSSQ2LpkREREQSJMU3mERERO8qNjYWXbt2hZaW1hsPOaogxu6Jtm3bIiUlBXZ2dvD19UVYWBguXryImJgYtG3bVvA8rVq1woQJExAVFaXsyL137x4mTpyINm3aAACysrLw2WefVXmWiIiIKr8H0YeA2/OJiIiIiIiI6H9KQ0MDBQUFMDU1hYbGm49TEWv3xLVr1/D06VM4OjqiuLgY48ePR3JyMmxsbLB48WJll6xQMjMz0b17d+Tm5sLMzAwymQz5+fmwsrLCnj17YGtri927d+PJkyfo16+foNmIPlUsmhIRERFJUGXb8l8l1sEZREREVDUUCgUOHTqEq1evQqFQoGnTpvjiiy/eWnQmoqrDoikRERGRBDk7O6t8XlpaitzcXFSrVg3W1tZITU0VKRkREdH7KS4uhq6urtgxiIjeC2eaEhEREUnQ+fPn1dYKCwsxcOBA9OjRQ4REREREf4+RkRFat24NT09PeHh4oH379iqnsNNLcXFxiIuLw927d9Vmm69fv16kVESfLnaaEhEREX1ALl26hK+++gp5eXliRyEiInonJ0+eRFJSEhITE3HixAmUlJSgZcuWyiJq165dxY4ouhkzZmDmzJlo3bo16tevD5lMpnJ9165dIiUj+nSxaEpERET0AUlOTka3bt3w559/ih2FiIjovcnlcpw5cwbh4eHYtGkTysvLRTkISmrq16+P+fPn85AnIgnh9nwiIiIiCVq2bJnK5wqFArdv38avv/4Kb29vkVIRERH9PVeuXEFiYqKy47S0tBTdunWDh4eH2NEk4cWLF3B1dRU7BhG9gp2mRERERBJkaWmp8rmGhgbq1KmDjh074ocffoC+vr5IyYiIiN5PvXr1UFpaio4dO8LT0xPu7u5wcHAQNZNcLkdkZOQbZ4jGx8cLmmfSpEmoWbMmpkyZIuh9iejN2GlKREREJEG5ubliRyAiIvqfqFevHjIyMpCfn4/8/Hz88ccfsLS0RM2aNUXLFBoaisjISPj6+qJ58+ZqM0SFVlJSgrVr1+Lo0aNwdHSElpaWyvVFixaJlIzo08VOUyIiIiKJ++OPPyCTydCwYUOxoxAREf0tjx49wrFjx5CUlISkpCRcvnwZjo6O8PLywrx58wTPU7t2bURFRcHHx0fwe1fGy8vrjddkMpngna9ExKIpERERkSSVl5dj9uzZWLhwIZ4+fQoA0NfXR1hYGH788UdoaGiInJCIiOj9PXz4EImJidizZw82b94s2kFQDRo0QGJiImxtbQW/NxF9GLg9n4iIiEiCfvzxR6xbtw7z5s2Dm5sbFAoFUlJSMH36dJSUlGDOnDliRyQiInonu3btQmJiIhITE3H58mXUqlULHTp0wOLFi9/aYVmVwsLC8P/au9PgqKt8/+OfX0hnBcIiDdKDIQEVQgIGAWUxhMCVMCggMJpRBItFFEdSCIheQXRYhrkuDMRxoLhArgyKMgSMykURSCSyZCAGZJc1bDFwYxIgBkN3/x+gPWmDjvo3faL9flVRRZ9u0u+iygd+Oef85s2bp1deecX40XwAtRM7TQEAAGqh5s2ba8GCBRowYIDX+ttvv61x48bp9OnThsoAAPhx7Ha7EhISlJiYqMTERMXGxhrpGDx4sNfrjRs3qlGjRmrXrl21O0QzMjJ80pOenq769etXa/s2X/QA8MZOUwAAgFqouLhYbdq0qbbepk0bFRcXGygCAOCnKSoqMp0gSYqIiPB6fc899xgquSoiIsKzy7V+/frseAVqGXaaAgAA1EK33XabbrvtNs2fP99r/fHHH9c///lPbdu2zVAZAAAA8OvHEwQAAABqof/6r//SkiVLFBMTo1GjRmn06NGKiYlRenq6XnjhBdN5AAD8oiUlJamkpKTaellZmZKSkvy+BwA7TQEAAGqt06dP69VXX9WBAwfkdrsVExOjcePGqXnz5qbTAAD4RQsICFBhYaHsdrvXelFRkRwOhyorK/26BwB3mgIAANRaDodDs2bNMp0BAMCvxu7duz2/37dvnwoLCz2vnU6n1q1bJ4fD4bc9AP6FnaYAAAC10NKlS1W3bl397ne/81pfuXKlysvLNWLECENlAAD8cgUEBHgeuHStcUhoaKjS0tI0cuRIv+wB8C8MTQEAAGqhm2++WQsWLFCvXr281rOzs/Xwww/r4MGDhsoAAPhxKioqlJaWpk2bNqmoqEgul8vr/by8PJ+1nDhxQm63W9HR0crNzVWTJk087wUFBclut6tOnTp+2wPgXzieDwAAUAudOHFCUVFR1dYjIyNVUFBgoAgAgJ9m5MiRWr9+vYYOHaouXbp4dlaaEBkZKUnVBremREZGqrKyUsOHD1ejRo08fQDMY2gKAABQC9ntdu3evVstW7b0Wt+1a5caN25sJgoAgJ/gvffe09q1a9W9e3ejHZmZmerXr59sNpsyMzO/97MDBgzwUZVks9n09ttv69lnn/XZdwL49xiaAgAA1EIpKSkaP3686tWrp4SEBElXj+anpqYqJSXFcB0AAD+cw+FQvXr1TGdo0KBBnifUDxo06Ds/Z1mWnE6n78J0tW3NmjV64oknfPq9AL4bd5oCAADUQl999ZUefPBBrVy5UoGBV/+d2+Vyafjw4VqwYIGCgoIMFwIA8MP87//+r+bPn68FCxZw/Pw7zJo1Sy+++KJ69+6tW2+9VeHh4V7vjx8/3lAZ4L8YmgIAANRin332mfLz8xUaGqq4uDj+ZxMA8Itz7tw53Xvvvfroo48UFhYmm83m9X5xcbHPm8rLyxUWFubz7/0u17rH/BuWZeno0aM+rAEgMTQFAAAAAAA1qE+fPiooKNCoUaPUtGnTag+CGjFihM+bgoKC1KlTJyUmJqpnz57q0aNHtd2dAPwbQ1MAAIBaaOjQoerUqZOeeuopr/UXXnhBubm5WrlypaEyAAB+nLCwMG3dulUdOnQwneKxdetWZWdnKysrS1u2bFFFRYU6duzoGaL269fPWNs3Y5pvD5cB+FaA6QAAAABUl52drf79+1dbT05O1kcffWSgCACAn6ZNmzb68ssvTWd46dq1q5566imtW7dOX3zxhT766CO1adNGL730ku666y4jTa+99pri4uIUGhqq0NBQtW/fXsuWLTPSAkAKNB0AAACA6i5evHjNhz3ZbDaVlZUZKAIA4KeZM2eOJk6cqFmzZikuLq7anab169c30nXgwAFlZWV5dpxWVlbq7rvvVs+ePX3e8vLLL2vatGn6wx/+oO7du8vtduvjjz/WI488ovPnz2vChAk+bwL8HcfzAQAAaqHOnTvr7rvv1rPPPuu1/txzz+mdd97Rzp07DZUBAPDjBARcPeT67ePmbrdblmXJ6XT6vKlZs2aqrKxUUlKSEhMTlZCQoLi4OJ93fCMqKkrPP/+8hg8f7rX+P//zP3ruued07NgxQ2WA/2KnKQAAQC00bdo0DRkyREeOHFFSUpIkacOGDXrjjTe4zxQA8IuyadMm0wnVNGvWTPv371dBQYEKCgp06tQpRUVFqW7dukZ6zp49q27dulVb79atm86ePWugCAA7TQEAAGqp9957T7Nnz1Z+fr7nbrPp06cbOTYIAMCvTUlJiT766CNlZ2crOztbe/fuVfv27dWrVy/NmTPHpy2xsbG6//779Z//+Z9e6zNnztSbb76pTz/91Kc9ABiaAgAA/OLk5+frlltuMZ0BAMAPtnnzZi1cuFBHjx7VypUr5XA4tGzZMkVFRalHjx5G24qLi5WVlaW3335br7/+ulwul8+vDFi1apXuu+8+9enTR927d5dlWcrJydGGDRv01ltv6Z577vFpDwApwHQAAAAA/r3S0lK9+uqr6tixo2699VbTOQAA/GCrVq1S3759FRoaqry8PF2+fFmSdOHCBc2ePdtI0+rVq5WamqoOHTrIbrfr0Ucf1aVLlzR37lzt3r3b5z1DhgzR9u3bdd1112nNmjXKyMjQddddp9zcXAamgCHsNAUAAKjFNm7cqMWLF2v16tWKjIzUkCFDNGTIEMXHx5tOAwDgB4mPj9eECRM0fPhw1atXT7t27VJ0dLTy8/OVnJyswsJCnzfZ7XYlJCQoMTFRiYmJio2N9XkDgNqNB0EBAADUMqdOnVJ6erqWLFmiS5cu6d5771VlZaVWrVqlmJgY03kAAPwoBw8eVEJCQrX1+vXrq6SkxPdBkoqKiox87/dxOp1avXq19u/fL8uy1LZtWw0cOFCBgYxuABP4Lw8AAKAW+e1vf6ucnBzdddddSktLU3JysurUqaMFCxaYTgMA4Ce5/vrrdfjwYbVs2dJrPScnR9HR0Waiapk9e/Zo4MCBKiws1M033yxJOnTokJo0aaLMzEzFxcUZLgT8D3eaAgAA1CIffPCBRo8ereeff179+/dXnTp1TCcBAPD/ZezYsUpNTdX27dtlWZbOnDmj5cuXa9KkSRo3bpzpvFph9OjRateunU6dOqW8vDzl5eXp5MmTat++vR5++GHTeYBfYqcpAABALbJ582YtWbJEnTp1Ups2bfTggw/qvvvuM50FAMBP9uSTT6q0tFS9evVSRUWFEhISFBwcrEmTJukPf/iD6bxaYdeuXdqxY4caNmzoWWvYsKFmzZqlzp07GywD/Bc7TQEAAGqRrl27atGiRTp79qzGjh2rFStWyOFwyOVyaf369bpw4YLpRAAAfrRZs2bp/Pnzys3N1bZt23Tu3DnNmDHDdFatcfPNN+vzzz+vtl5UVKTWrVsbKAJgud1ut+kIAAAAfLeDBw9q8eLFWrZsmUpKSvQf//EfyszMNJ0FAAB+JmvXrtWTTz6p5557Trfffrskadu2bfrjH/+oOXPmqEePHp7P1q9f31Qm4FcYmgIAAPxCOJ1OvfPOO1qyZAlDUwBArTZ48OAf/NmMjIwaLLm2iooKpaWladOmTSoqKpLL5fJ6Py8vz6c9AQH/OghsWZYk6ZtxTdXXlmXJ6XT6tA3wV9xpCgAA8AtRp04dDRo0SIMGDTKdAgDA94qIiDCd8L1Gjhyp9evXa+jQoerSpYtnMGnKpk2bjH4/gOrYaQoAAAAAAPxKRESE1q5dq+7du5tOAVBL8SAoAAAAAABQ44qKirR582bl5OSoqKjIaIvD4VC9evWMNnzb5s2bNWzYMHXr1k2nT5+WJC1btkw5OTmGywD/xNAUAAAAAADUmLKyMj344INyOBzq2bOnEhIS5HA4NGzYMJWWlhppeumllzRlyhSdOHHCyPd/26pVq9S3b1+FhoYqLy9Ply9fliRduHBBs2fPNlwH+CeGpgAAAAAAoMaMHj1a27dv17vvvquSkhKVlpbq3Xff1Y4dOzRmzBgjTZ06dVJFRYWio6NVr149NWrUyOuXr82cOVMLFizQokWLZLPZPOvdunXz+UOpAFzFg6AAAAAAAECNee+99/T++++rR48enrW+fftq0aJFSk5ONtL0+9//XqdPn9bs2bPVtGlT4w+COnjwoBISEqqt169fXyUlJb4PAsDQFAAAAAAA1JzGjRsrIiKi2npERIQaNmxooEjasmWLtm7dqg4dOhj5/m+7/vrrdfjwYbVs2dJrPScnR9HR0WaiAD/H8XwAAAAAAFBjpk6dqieeeEJnz571rBUWFmry5MmaNm2akaY2bdroyy+/NPLd1zJ27FilpqZq+/btsixLZ86c0fLlyzVp0iSNGzfOdB7glyy32+02HQEAAAAAAH494uPjvY68f/bZZ7p8+bJuuOEGSVJBQYGCg4N14403Grmz84MPPtDzzz+vWbNmKS4uzuseUenqsXhfe+aZZzR37lxVVFRIkoKDgzVp0iTNmDHD5y0AGJoCAAAAAICf2fPPP/+DPzt9+vQaLLm2gICrB2+/fZep2+2WZVlyOp0+a3E6ncrJyVFcXJxCQkK0b98+uVwuxcTEqG7duj7rAOCNoSkAAAAAAPAr2dnZ3/t+z549fVRyVUhIiPbv36+oqCiffi+A78aDoAAAAAAAQI3buXOn9u/fL8uyFBMTo/j4eGMtvh6K/jtxcXE6evQoQ1OgFmGnKQAAAAAAqDFFRUVKSUlRVlaWGjRoILfbrdLSUvXq1UsrVqxQkyZNjHRt3rxZCxcu1NGjR7Vy5Uo5HA4tW7ZMUVFR6tGjh09bPvjgA02ZMkUzZszQrbfeqvDwcK/3TdyxCvi7ANMBAAAAAADg1+vxxx9XWVmZ9u7dq+LiYn3xxRfas2ePysrKNH78eCNNq1atUt++fRUaGqq8vDxdvnxZknThwgXNnj3b5z3JycnatWuXBgwYoN/85jdq2LChGjZsqAYNGqhhw4Y+7wHATlMAAAAAAFCDIiIi9OGHH6pz585e67m5ubrzzjtVUlLi86b4+HhNmDBBw4cPV7169bRr1y5FR0crPz9fycnJKiws9GlPbbtjFQB3mgIAAAAAgBrkcrlks9mqrdtsNrlcLgNF0sGDB5WQkFBtvX79+kaGuAxFgdqH4/kAAAAAAKDGJCUlKTU1VWfOnPGsnT59WhMmTFDv3r2NNF1//fU6fPhwtfWcnBxFR0cbKAJQ2zA0BQAAAAAANeaVV17RhQsX1LJlS7Vq1UqtW7dWVFSULly4oLS0NCNNY8eOVWpqqrZv3y7LsnTmzBktX75ckyZN0rhx44w0AahduNMUAAAAAADUuPXr1+vAgQNyu92KiYlRnz59jPY888wzmjt3rioqKiRJwcHBmjRpkmbMmGG0C0DtwNAUAAAAAADUiCtXrigkJET5+fmKjY01nVNNeXm59u3bJ5fLpZiYGNWtW9d0EoBaguP5AAAAAACgRgQGBioyMlJOp9N0yjWFhYWpU6dO6tKli9GBaVJS0jUfQFVWVqakpCTfBwFgpykAAAAAAKg5S5cu1cqVK/X3v/9djRo1MtYxePDgH/zZjIyMGiypLiAgQIWFhbLb7V7rRUVFcjgcqqys9GkPACnQdAAAAAAAAPj1mj9/vg4fPqzmzZsrMjJS4eHhXu/n5eX5pCMiIsIn3/Nj7N692/P7ffv2qbCw0PPa6XRq3bp1cjgcJtIAv8fQFAAAAAAA1JiBAwfKsizTGVq6dKnphGpuueUWWZYly7KueQw/NDRUaWlpBsoAcDwfAAAAAAD4paKiIh08eFCWZemmm26qdjy+pp04cUJut1vR0dHKzc1VkyZNPO8FBQXJbrerTp06Pm0CcBVDUwAAAAAA8LMrLy/X5MmTtWbNGlVWVqpPnz6aP3++rrvuOtNpKisr02OPPaYVK1Z4HlJVp04d3XffffrrX/9aK4/yA/AthqYAAAAAAOBnN3nyZL366qt64IEHFBISojfeeEOJiYlauXKl6TTde++9ys/PV1pamrp27SrLsrRlyxalpqaqffv2euutt2q8ITMzU/369ZPNZlNmZub3fnbAgAE13gPAG0NTAAAAAADws2vVqpVmzZqllJQUSVJubq66d++uiooK40fOw8PD9f7776tHjx5e65s3b1ZycrIuXbpU4w0BAQEqLCyU3W5XQEDAd37OsizPblgAvsODoAAAAAAAwM/u5MmTuuOOOzyvu3TposDAQJ05c0YtWrQwWCY1btz4mkfwIyIi1LBhQ580uFyua/4eQO3w3f+UAQAAAAAA8BM5nU4FBQV5rQUGBurKlSuGiv5l6tSpeuKJJ3T27FnPWmFhoSZPnqxp06YZLANQW3A8HwAAAAAA/OwCAgLUr18/BQcHe9beeecdJSUlKTw83LOWkZHhk574+HhZluV5/dlnn+ny5cu64YYbJEkFBQUKDg7WjTfeqLy8vBrvmT9//g/+7Pjx42uwBMC1cDwfAAAAAAD87EaMGFFtbdiwYQZKrho0aJCx776WuXPner0+d+6cysvL1aBBA0lSSUmJwsLCZLfbGZoCBrDTFAAAAAAAwKDXX39dr776qhYvXqybb75ZknTw4EGNGTNGY8eO1QMPPGC4EPA/DE0BAAAAAIBf2rlzp/bv3y/LshQTE6P4+HgjHa1atdI//vGPat+/c+dODR06VMeOHTPSBfgzjucDAAAAAAC/UlRUpJSUFGVlZalBgwZyu90qLS1Vr169tGLFCjVp0sSnPWfPnlVlZWW1dafTqc8//9ynLQCuCjAdAAAAAAAA4EuPP/64ysrKtHfvXhUXF+uLL77Qnj17VFZWZuT+0N69e2vMmDHasWOHvjkQvGPHDo0dO1Z9+vTxeQ8AjucDAAAAAAA/ExERoQ8//FCdO3f2Ws/NzdWdd96pkpISn/acO3dOI0aM0Lp162Sz2SRJV65cUd++fZWeni673e7THgAczwcAAAAAAH7G5XJ5hpNV2Ww2uVwun/c0adJEa9eu1aFDh3TgwAG53W61bdtWN910k89bAFzFTlMAAAAAAOBXBg4cqJKSEr3xxhtq3ry5JOn06dN64IEH1LBhQ61evdpI11dffaVjx46pVatWCgxknxtgEneaAgAAAAAAv/LKK6/owoULatmypVq1aqXWrVsrKipKFy5cUFpams97ysvLNWrUKIWFhaldu3YqKCiQJI0fP15z5szxeQ8AdpoCAAAAAAA/tX79es9x+JiYGGMPXUpNTdXHH3+sv/zlL0pOTtbu3bsVHR2tzMxMTZ8+XZ988omRLsCfMTQFAAAAAAB+48qVKwoJCVF+fr5iY2NN50iSIiMj9eabb+r2229XvXr1tGvXLkVHR+vw4cPq2LGjysrKTCcCfofj+QAAAAAAwG8EBgYqMjJSTqfTdIrHuXPnZLfbq61funRJlmUZKALA0BQAAAAAAPiVqVOn6umnn1ZxcbHpFElS586d9d5773lefzMoXbRokbp27WoqC/BrPIoNAAAAAAD4lfnz5+vw4cNq3ry5IiMjFR4e7vV+Xl6eT3v+9Kc/KTk5Wfv27dOVK1c0b9487d27V1u3blV2drZPWwBcxdAUAAAAAAD4lYEDB9aqY+/dunXTli1b9MILL6hVq1b64IMP1LFjR23dulVxcXGm8wC/xIOgAAAAAAAADKmsrNTDDz+sadOmKTo62nQOgK9xpykAAAAAAPAL5eXleuyxx+RwOGS323X//ffr/PnzRptsNptWr15ttAFAdQxNAQAAAACAX5g+fbrS09PVv39/paSkaP369Xr00UdNZ+mee+7RmjVrTGcAqII7TQEAAAAAgF/IyMjQ4sWLlZKSIkkaNmyYunfvLqfTqTp16hjrat26tWbMmKEtW7bo1ltvrfZgqvHjxxsqA/wXd5oCAAAAAAC/EBQUpGPHjsnhcHjWQkNDdejQIbVo0cJYV1RU1He+Z1mWjh496sMaABI7TQEAAAAAgJ9wOp0KCgryWgsMDNSVK1cMFV117Ngxo98PoDqGpgAAAAAAwC+43W499NBDCg4O9qxVVFTokUce8ToSn5GRYSIPQC3C0BQAAAAAAPiFESNGVFsbNmyYgRJvQ4cOVadOnfTUU095rb/wwgvKzc3VypUrDZUB/os7TQEAAAAAAAxq0qSJNm7cqLi4OK/1Tz/9VH369NHnn39uqAzwXwGmAwA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+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix, classification_report\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Adjust the confusion matrix call to work with numeric labels\n",
+    "cm = confusion_matrix(true_categories, predicted_categories, labels=list(id2label.keys()))\n",
+    "\n",
+    "# Convert numeric labels back to category names for visualization\n",
+    "product_categories = list(id2label.values())\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 8))\n",
+    "sns.heatmap(cm, annot=True, fmt='d', ax=ax, cmap='Blues', xticklabels=product_categories, yticklabels=product_categories)\n",
+    "plt.ylabel('Actual')\n",
+    "plt.xlabel('Predicted')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "bc8b05da-d854-488f-906b-7036f1371104",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                                                  precision    recall  f1-score   support\n",
+      "\n",
+      "                          Account Operations and Unauthorized Transaction Issues       0.56      0.32      0.41       619\n",
+      "                                               Attempts to collect debt not owed       0.54      0.57      0.56      1381\n",
+      "                                                              Closing an account       0.68      0.64      0.66       521\n",
+      "                                                            Closing your account       0.58      0.64      0.61       191\n",
+      "                                             Credit Report and Monitoring Issues       0.67      0.53      0.59       412\n",
+      "                                            Dealing with your lender or servicer       0.96      0.97      0.97       314\n",
+      "                                                 Disputes and Misrepresentations       0.50      0.38      0.43       550\n",
+      "                                                     Improper use of your report       0.84      0.87      0.85     15434\n",
+      "                                            Incorrect information on your report       0.81      0.86      0.83     21807\n",
+      "                                                        Legal and Threat Actions       0.00      0.00      0.00       184\n",
+      "                                                             Managing an account       0.70      0.77      0.73      1826\n",
+      "                                                    Payment and Funds Management       0.53      0.56      0.54       530\n",
+      "                   Problem with a company's investigation into an existing issue       0.00      0.00      0.00        53\n",
+      "                 Problem with a company's investigation into an existing problem       0.64      0.46      0.53      5117\n",
+      "Problem with a credit reporting company's investigation into an existing problem       0.69      0.70      0.70     11522\n",
+      "                                 Problem with a purchase shown on your statement       0.73      0.77      0.75       841\n",
+      "                                                 Written notification about debt       0.66      0.44      0.53       578\n",
+      "\n",
+      "                                                                        accuracy                           0.77     61880\n",
+      "                                                                       macro avg       0.59      0.56      0.57     61880\n",
+      "                                                                    weighted avg       0.76      0.77      0.76     61880\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "/home/kudupudi.n/.conda/envs/pytorch_env/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n"
+     ]
+    }
+   ],
+   "source": [
+    "report = classification_report(true_categories, predicted_categories, target_names=product_categories)\n",
+    "print(report)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b3b390f4-14ef-4be3-9cd0-885d69bdafc8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "true_labels_text = [id2label[label] for label in true_categories]\n",
+    "predicted_labels_text = [id2label[label] for label in predicted_categories]\n",
+    "\n",
+    "results_df = pd.DataFrame({\n",
+    "    'True Labels': true_labels_text,\n",
+    "    'Predicted Labels': predicted_labels_text\n",
+    "})\n",
+    "\n",
+    "original_texts = [tokenizer.decode(ids, skip_special_tokens=True) for ids in tokenized_data[\"test\"][\"input_ids\"]]\n",
+    "results_df['Complaint Narrative'] = original_texts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "cc2584eb-cc99-4526-9eba-46902ce94fe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_df.to_csv('distil-bert-fintuned-issue-cfpb-complaints/saved_results/model_predictions_and_labels.csv', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}