@classmethod
def _load(cls, model_path: Path, config: Dict[(Text, Any)], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext) -> 'DIETClassifier':
    'Loads the trained model from the provided directory.'
    (index_label_id_mapping, entity_tag_specs, label_data, data_example, sparse_feature_sizes) = cls._load_from_files(model_path)
    config = train_utils.update_confidence_type(config)
    config = train_utils.update_similarity_type(config)
    model = cls._load_model(entity_tag_specs, label_data, config, data_example, model_path, finetune_mode=execution_context.is_finetuning)
    return cls(config=config, model_storage=model_storage, resource=resource, execution_context=execution_context, index_label_id_mapping=index_label_id_mapping, entity_tag_specs=entity_tag_specs, model=model, sparse_feature_sizes=sparse_feature_sizes)
Loads the trained model from the provided directory.
@classmethod
def _load(cls, model_path: Path, config: Dict[(Text, Any)], model_storage: ModelStorage, resource: Resource, execution_context: ExecutionContext) -> 'DIETClassifier':
    
    (index_label_id_mapping, entity_tag_specs, label_data, data_example, sparse_feature_sizes) = cls._load_from_files(model_path)
    config = train_utils.update_confidence_type(config)
    config = train_utils.update_similarity_type(config)
    model = cls._load_model(entity_tag_specs, label_data, config, data_example, model_path, finetune_mode=execution_context.is_finetuning)
    return cls(config=config, model_storage=model_storage, resource=resource, execution_context=execution_context, index_label_id_mapping=index_label_id_mapping, entity_tag_specs=entity_tag_specs, model=model, sparse_feature_sizes=sparse_feature_sizes)