code: def optional_data_names(
      cls, train: bool = True, inference: bool = False
  ) -> Tuple[str, ...]:
      retval = [
          "enroll_ref{}".format(n)
          for n in range(2, MAX_REFERENCE_NUM + 1)
      ]
      if "speech_ref1" in retval:
          retval += [
              "speech_ref{}".format(n)
              for n in range(2, MAX_REFERENCE_NUM + 1)
          ]
      else:
          retval += [
              "speech_ref{}".format(n)
              for n in range(1, MAX_REFERENCE_NUM + 1)
          ]
      retval += ["category"]
      retval = tuple(retval)
      assert check_return_type(retval)
      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 5 for calling the TargetSpeakerExtractionTask obj's underlying member method code with example usage: obj.optional_data_names(cls, train, inference) and returns: retval