class_docstr: Custom batch converter for Pytorch.
  
  Args:
      subsampling_factor (int): The subsampling factor.
      dtype (torch.dtype): Data type to convert.
      use_source_text (bool): use source transcription.
class_docstr_tok: ["Custom","batch","converter","for","Pytorch",".","Args",":","subsampling_factor","(","int",")",":","The","subsampling","factor",".","dtype","(","torch.dtype",")",":","Data","type","to","convert",".","use_source_text","(","bool",")",":","use","source","transcription","."]
docstr: Construct a CustomConverter object.
docstr_tok: ["Construct","a","CustomConverter","object","."]
code: def __init__(
      self,
      subsampling_factor=1,
      dtype=torch.float32,
      use_source_text=False,
  ):
      """Construct a CustomConverter object."""
      super().__init__(
          subsampling_factor=subsampling_factor, dtype=dtype
      )
      self.use_source_text = use_source_text
  
imports: import itertools
  import json
  import logging
  import os
  import numpy
  import torch
  from chainer import training
  from chainer.training import extensions
  from espnet.asr.asr_utils import CompareValueTrigger, adadelta_eps_decay, adam_lr_decay, add_results_to_json, restore_snapshot, snapshot_object, torch_load, torch_resume, torch_snapshot
  from espnet.asr.pytorch_backend.asr import CustomConverter
  from espnet.asr.pytorch_backend.asr import CustomEvaluator, CustomUpdater
  from espnet.asr.pytorch_backend.asr_init import load_trained_model, load_trained_modules
  from espnet.nets.pytorch_backend.e2e_asr import pad_list
  from espnet.nets.st_interface import STInterface
  from espnet.utils.dataset import ChainerDataLoader, TransformDataset
  from espnet.utils.deterministic_utils import set_deterministic_pytorch
  from espnet.utils.dynamic_import import dynamic_import
  from espnet.utils.io_utils import LoadInputsAndTargets
  from espnet.utils.training.batchfy import make_batchset
  from espnet.utils.training.iterators import ShufflingEnabler
  from espnet.utils.training.tensorboard_logger import TensorboardLogger
  from espnet.utils.training.train_utils import check_early_stop, set_early_stop
label_desc: Use image node_id 1 to create a new CustomConverter object from inherited base classes: ASRCustomConverter with example: obj = CustomConverter(subsampling_factor, dtype, use_source_text)