code: def build_preprocess_fn(
      cls, args: argparse.Namespace, train: bool
  ) -> Optional[
      Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]
  ]:
      assert check_argument_types()
      kwargs = dict(
          train_spk2enroll=args.train_spk2enroll,
          enroll_segment=getattr(args, "enroll_segment", None),
          load_spk_embedding=getattr(args, "load_spk_embedding", False),
          load_all_speakers=getattr(args, "load_all_speakers", False),
          # inherited from EnhPreprocessor
          rir_scp=getattr(args, "rir_scp", None),
          rir_apply_prob=getattr(args, "rir_apply_prob", 1.0),
          noise_scp=getattr(args, "noise_scp", None),
          noise_apply_prob=getattr(args, "noise_apply_prob", 1.0),
          noise_db_range=getattr(args, "noise_db_range", "13_15"),
          short_noise_thres=getattr(args, "short_noise_thres", 0.5),
          speech_volume_normalize=getattr(
              args, "speech_volume_normalize", None
          ),
          use_reverberant_ref=getattr(
              args, "use_reverberant_ref", None
          ),
          num_spk=getattr(args, "num_spk", 1),
          num_noise_type=getattr(args, "num_noise_type", 1),
          sample_rate=getattr(args, "sample_rate", 8000),
          force_single_channel=getattr(
              args, "force_single_channel", False
          ),
          channel_reordering=getattr(args, "channel_reordering", False),
          categories=getattr(args, "categories", None),
          speech_segment=getattr(args, "speech_segment", None),
          avoid_allzero_segment=getattr(
              args, "avoid_allzero_segment", True
          ),
          flexible_numspk=getattr(args, "flexible_numspk", False),
      )
      kwargs.update(args.preprocessor_conf)
      retval = TSEPreprocessor(train=train, **kwargs)
      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 3 for calling the TargetSpeakerExtractionTask obj's underlying member method code with example usage: obj.build_preprocess_fn(cls, args, train) and returns: retval