class_docstr: Custom batch converter for Pytorch.
class_docstr_tok: ["Custom","batch","converter","for","Pytorch","."]
docstr: Construct a CustomConverter object.
docstr_tok: ["Construct","a","CustomConverter","object","."]
code: def __init__(self):
      """Construct a CustomConverter object."""
      self.ignore_id = -1
      self.pad = 0
  
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 CustomEvaluator, CustomUpdater, load_trained_model
  from espnet.nets.mt_interface import MTInterface
  from espnet.nets.pytorch_backend.e2e_asr import pad_list
  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: object with example: obj = CustomConverter()