code: def build_collate_fn(
      cls, args: argparse.Namespace, train: bool
  ) -> Callable[
      [Collection[Tuple[str, Dict[str, np.ndarray]]]],
      Tuple[List[str], Dict[str, torch.Tensor]],
  ]:
      assert check_argument_types()
  
      # default sampling rate is 16000
      fs = args.frontend_conf.get("fs", 16000)
      if isinstance(fs, str):
          fs = humanfriendly.parse_size(fs)
      sample_rate = fs / 1000
  
      if (
          args.encoder_conf.get("extractor_conv_layer_config", None)
          is None
      ):
          # corresponding to default conv extractor
          # refer to espnet2/asr/encoder/hubert_encoder.py
          reception_field = 400
          stride_field = 320
      else:
          stride_field, reception_field = 1, 1
          for conv_config in args.encoder_conf[
              "extractor_conv_layer_config"
          ][::-1]:
              _, kernel, stride = conv_config
              stride_field *= stride
              reception_field = stride * (reception_field - 1) + kernel
  
      window_size = reception_field / sample_rate
      window_shift = stride_field / sample_rate
      return HuBERTCollateFn(
          float_pad_value=0.0,
          int_pad_value=-1,
          label_downsampling=args.collate_fn_conf.get(
              "label_downsampling", 1
          ),
          pad=args.collate_fn_conf.get("pad", False),
          rand_crop=args.collate_fn_conf.get("rand_crop", True),
          crop_audio=not args.collect_stats,
          window_size=window_size,
          window_shift=window_shift,
          sample_rate=sample_rate,
      )
  
imports: import argparse
  import logging
  from typing import Callable, Collection, Dict, List, Optional, Tuple, Union
  import humanfriendly
  import numpy
  import torch
  from typeguard import check_argument_types, check_return_type
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet2.asr.encoder.hubert_encoder import FairseqHubertPretrainEncoder, TorchAudioHuBERTPretrainEncoder
  from espnet2.asr.frontend.abs_frontend import AbsFrontend
  from espnet2.asr.frontend.default import DefaultFrontend
  from espnet2.asr.frontend.windowing import SlidingWindow
  from espnet2.asr.preencoder.abs_preencoder import AbsPreEncoder
  from espnet2.asr.preencoder.sinc import LightweightSincConvs
  from espnet2.asr.specaug.abs_specaug import AbsSpecAug
  from espnet2.asr.specaug.specaug import SpecAug
  from espnet2.hubert.espnet_model import HubertPretrainModel, TorchAudioHubertPretrainModel
  from espnet2.layers.abs_normalize import AbsNormalize
  from espnet2.layers.global_mvn import GlobalMVN
  from espnet2.layers.utterance_mvn import UtteranceMVN
  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.abs_espnet_model import AbsESPnetModel
  from espnet2.train.class_choices import ClassChoices
  from espnet2.train.collate_fn import HuBERTCollateFn
  from espnet2.train.preprocessor import CommonPreprocessor
  from espnet2.train.trainer import Trainer
  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 2 for calling the HubertTask obj's underlying member method code with example usage: obj.build_collate_fn(cls, args, train) and returns: HuBERTCollateFn