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()
      if args.src_token_type == "none":
          args.src_token_type = None
  
      if args.use_preprocessor:
          try:
              _ = getattr(args, "preprocessor")
          except AttributeError:
              setattr(args, "preprocessor", "default")
              setattr(args, "preprocessor_conf", dict())
          except Exception as e:
              raise e
  
          retval = MutliTokenizerCommonPreprocessor(
              train=train,
              token_type=[args.token_type, args.src_token_type],
              token_list=[args.token_list, args.src_token_list],
              bpemodel=[args.bpemodel, args.src_bpemodel],
              non_linguistic_symbols=args.non_linguistic_symbols,
              text_cleaner=args.cleaner,
              g2p_type=[args.g2p, args.src_g2p],
              # NOTE(kamo): Check attribute existence for backward compatibility
              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
              ),
              speech_name="speech",
              text_name=["text", "src_text"],
              **getattr(args, "preprocessor_conf", {}),
          )
      else:
          retval = None
      assert check_return_type(retval)
      return retval
  
imports: import argparse
  import logging
  from typing import Callable, Collection, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from typeguard import check_argument_types, check_return_type
  from espnet2.asr.ctc import CTC
  from espnet2.asr.decoder.abs_decoder import AbsDecoder
  from espnet2.asr.decoder.hugging_face_transformers_decoder import HuggingFaceTransformersDecoder
  from espnet2.asr.decoder.rnn_decoder import RNNDecoder
  from espnet2.asr.decoder.transducer_decoder import TransducerDecoder
  from espnet2.asr.decoder.transformer_decoder import DynamicConvolution2DTransformerDecoder, DynamicConvolutionTransformerDecoder, LightweightConvolution2DTransformerDecoder, LightweightConvolutionTransformerDecoder, TransformerDecoder, TransformerMDDecoder
  from espnet2.asr.decoder.whisper_decoder import OpenAIWhisperDecoder
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet2.asr.encoder.branchformer_encoder import BranchformerEncoder
  from espnet2.asr.encoder.conformer_encoder import ConformerEncoder
  from espnet2.asr.encoder.contextual_block_conformer_encoder import ContextualBlockConformerEncoder
  from espnet2.asr.encoder.contextual_block_transformer_encoder import ContextualBlockTransformerEncoder
  from espnet2.asr.encoder.e_branchformer_encoder import EBranchformerEncoder
  from espnet2.asr.encoder.hubert_encoder import FairseqHubertEncoder, FairseqHubertPretrainEncoder
  from espnet2.asr.encoder.hugging_face_transformers_encoder import HuggingFaceTransformersEncoder
  from espnet2.asr.encoder.rnn_encoder import RNNEncoder
  from espnet2.asr.encoder.transformer_encoder import TransformerEncoder
  from espnet2.asr.encoder.vgg_rnn_encoder import VGGRNNEncoder
  from espnet2.asr.encoder.wav2vec2_encoder import FairSeqWav2Vec2Encoder
  from espnet2.asr.encoder.whisper_encoder import OpenAIWhisperEncoder
  from espnet2.asr.frontend.abs_frontend import AbsFrontend
  from espnet2.asr.frontend.default import DefaultFrontend
  from espnet2.asr.frontend.s3prl import S3prlFrontend
  from espnet2.asr.frontend.windowing import SlidingWindow
  from espnet2.asr.postencoder.abs_postencoder import AbsPostEncoder
  from espnet2.asr.postencoder.hugging_face_transformers_postencoder import HuggingFaceTransformersPostEncoder
  from espnet2.asr.postencoder.length_adaptor_postencoder import LengthAdaptorPostEncoder
  from espnet2.asr.preencoder.abs_preencoder import AbsPreEncoder
  from espnet2.asr.preencoder.linear import LinearProjection
  from espnet2.asr.preencoder.sinc import LightweightSincConvs
  from espnet2.asr.specaug.abs_specaug import AbsSpecAug
  from espnet2.asr.specaug.specaug import SpecAug
  from espnet2.asr_transducer.joint_network import JointNetwork
  from espnet2.layers.abs_normalize import AbsNormalize
  from espnet2.layers.global_mvn import GlobalMVN
  from espnet2.layers.utterance_mvn import UtteranceMVN
  from espnet2.st.espnet_model import ESPnetSTModel
  from espnet2.tasks.abs_task import AbsTask
  from espnet2.text.phoneme_tokenizer import g2p_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, MutliTokenizerCommonPreprocessor
  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 float_or_none, int_or_none, str2bool, str_or_none
label_desc: Use image node_id 3 for calling the STTask obj's underlying member method code with example usage: obj.build_preprocess_fn(cls, args, train) and returns: retval