code: def required_data_names(
      cls, train: bool = True, inference: bool = False
  ) -> Tuple[str, ...]:
      if not inference:
          retval = ("speech_mix", "enroll_ref1", "speech_ref1")
      else:
          # Inference mode
          retval = ("speech_mix", "enroll_ref1")
      return retval
  
imports: import argparse
  from typing import Callable, Collection, Dict, List, Optional, Tuple
  import numpy
  import torch
  from typeguard import check_argument_types, check_return_type
  from espnet2.enh.espnet_model_tse import ESPnetExtractionModel
  from espnet2.enh.extractor.abs_extractor import AbsExtractor
  from espnet2.enh.extractor.td_speakerbeam_extractor import TDSpeakerBeamExtractor
  from espnet2.tasks.abs_task import AbsTask
  from espnet2.tasks.enh import criterion_choices, decoder_choices, encoder_choices, loss_wrapper_choices
  from espnet2.torch_utils.initialize import initialize
  from espnet2.train.class_choices import ClassChoices
  from espnet2.train.collate_fn import CommonCollateFn
  from espnet2.train.preprocessor import AbsPreprocessor, TSEPreprocessor
  from espnet2.train.trainer import Trainer
  from espnet2.utils.get_default_kwargs import get_default_kwargs
  from espnet2.utils.nested_dict_action import NestedDictAction
  from espnet2.utils.types import int_or_none, str2bool, str_or_none
label_desc: Use image node_id 4 for calling the TargetSpeakerExtractionTask obj's underlying member method code with example usage: obj.required_data_names(cls, train, inference) and returns: retval