@classmethod
def _load_from_files(cls, model_path: Path) -> Tuple[(Dict[(int, Text)], List[EntityTagSpec], RasaModelData, Dict[(Text, Dict[(Text, List[FeatureArray])])], Dict[(Text, Dict[(Text, List[int])])])]:
    file_name = cls.__name__
    data_example = io_utils.pickle_load((model_path / f'{file_name}.data_example.pkl'))
    label_data = io_utils.pickle_load((model_path / f'{file_name}.label_data.pkl'))
    label_data = RasaModelData(data=label_data)
    sparse_feature_sizes = io_utils.pickle_load((model_path / f'{file_name}.sparse_feature_sizes.pkl'))
    index_label_id_mapping = io_utils.json_unpickle((model_path / f'{file_name}.index_label_id_mapping.json'))
    entity_tag_specs = rasa.shared.utils.io.read_json_file((model_path / f'{file_name}.entity_tag_specs.json'))
    entity_tag_specs = [EntityTagSpec(tag_name=tag_spec['tag_name'], ids_to_tags={int(key): value for (key, value) in tag_spec['ids_to_tags'].items()}, tags_to_ids={key: int(value) for (key, value) in tag_spec['tags_to_ids'].items()}, num_tags=tag_spec['num_tags']) for tag_spec in entity_tag_specs]
    index_label_id_mapping = {int(key): value for (key, value) in index_label_id_mapping.items()}
    return (index_label_id_mapping, entity_tag_specs, label_data, data_example, sparse_feature_sizes)
@classmethod
def _load_from_files(cls, model_path: Path) -> Tuple[(Dict[(int, Text)], List[EntityTagSpec], RasaModelData, Dict[(Text, Dict[(Text, List[FeatureArray])])], Dict[(Text, Dict[(Text, List[int])])])]:
    file_name = cls.__name__
    data_example = io_utils.pickle_load((model_path / f'{file_name}.data_example.pkl'))
    label_data = io_utils.pickle_load((model_path / f'{file_name}.label_data.pkl'))
    label_data = RasaModelData(data=label_data)
    sparse_feature_sizes = io_utils.pickle_load((model_path / f'{file_name}.sparse_feature_sizes.pkl'))
    index_label_id_mapping = io_utils.json_unpickle((model_path / f'{file_name}.index_label_id_mapping.json'))
    entity_tag_specs = rasa.shared.utils.io.read_json_file((model_path / f'{file_name}.entity_tag_specs.json'))
    entity_tag_specs = [EntityTagSpec(tag_name=tag_spec['tag_name'], ids_to_tags={int(key): value for (key, value) in tag_spec['ids_to_tags'].items()}, tags_to_ids={key: int(value) for (key, value) in tag_spec['tags_to_ids'].items()}, num_tags=tag_spec['num_tags']) for tag_spec in entity_tag_specs]
    index_label_id_mapping = {int(key): value for (key, value) in index_label_id_mapping.items()}
    return (index_label_id_mapping, entity_tag_specs, label_data, data_example, sparse_feature_sizes)