{"action":"created","comment":{"url":"https://api.github.com/repos/deepmipt/DeepPavlov/pulls/comments/243212193","pull_request_review_id":186919681,"id":243212193,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDI0MzIxMjE5Mw==","diff_hunk":"@@ -0,0 +1,518 @@\n+# Copyright 2017 Neural Networks and Deep Learning lab, MIPT\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+#     http://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+import os\n+import time\n+from copy import copy, deepcopy\n+from datetime import datetime\n+from multiprocessing import Pool\n+from pathlib import Path\n+from shutil import rmtree\n+from typing import Union, Dict, Generator, Optional, List\n+\n+from psutil import cpu_count\n+from tqdm import tqdm\n+\n+from deeppavlov.core.commands.train import read_data_by_config, get_iterator_from_config\n+from deeppavlov.core.commands.train import train_evaluate_model_from_config\n+from deeppavlov.core.commands.utils import expand_path, parse_config\n+from deeppavlov.core.common.errors import ConfigError\n+from deeppavlov.core.common.file import read_json\n+from deeppavlov.core.common.prints import RedirectedPrints\n+from deeppavlov.core.data.data_fitting_iterator import DataFittingIterator\n+from deeppavlov.pipeline_manager.observer import Observer\n+from deeppavlov.pipeline_manager.pipegen import PipeGen\n+from deeppavlov.pipeline_manager.utils import get_num_gpu\n+from deeppavlov.pipeline_manager.utils import results_visualization, get_available_gpus\n+\n+\n+def unpack_args(func):\n+    \"\"\"\n+    Decorator that unpacks input arguments from tuple or dict.\n+    \"\"\"\n+    from functools import wraps\n+\n+    @wraps(func)\n+    def wrapper(args):\n+        if isinstance(args, dict):\n+            return func(**args)\n+        else:\n+            return func(*args)\n+\n+    return wrapper\n+\n+\n+class 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