@classmethod
def _load_model_class(cls, tf_model_file: Text, model_data_example: RasaModelData, label_data: RasaModelData, entity_tag_specs: List[EntityTagSpec], config: Dict[(Text, Any)], finetune_mode: bool) -> 'RasaModel':
    predict_data_example = RasaModelData(label_key=model_data_example.label_key, data={feature_name: features for (feature_name, features) in model_data_example.items() if (TEXT in feature_name)})
    return cls.model_class().load(tf_model_file, model_data_example, predict_data_example, data_signature=model_data_example.get_signature(), label_data=label_data, entity_tag_specs=entity_tag_specs, config=copy.deepcopy(config), finetune_mode=finetune_mode)
@classmethod
def _load_model_class(cls, tf_model_file: Text, model_data_example: RasaModelData, label_data: RasaModelData, entity_tag_specs: List[EntityTagSpec], config: Dict[(Text, Any)], finetune_mode: bool) -> 'RasaModel':
    predict_data_example = RasaModelData(label_key=model_data_example.label_key, data={feature_name: features for (feature_name, features) in model_data_example.items() if (TEXT in feature_name)})
    return cls.model_class().load(tf_model_file, model_data_example, predict_data_example, data_signature=model_data_example.get_signature(), label_data=label_data, entity_tag_specs=entity_tag_specs, config=copy.deepcopy(config), finetune_mode=finetune_mode)