class_docstr: Custom converter.
class_docstr_tok: ["Custom","converter","."]
docstr: Initilize module.
docstr_tok: ["Initilize","module","."]
code: def __init__(self):
      """Initilize module."""
      # NOTE: keep as class for future development
      pass
  
imports: import copy
  import json
  import logging
  import math
  import os
  import time
  import chainer
  import kaldiio
  import numpy
  import torch
  from chainer import training
  from chainer.training import extensions
  from espnet.asr.asr_utils import get_model_conf, snapshot_object, torch_load, torch_resume, torch_snapshot
  from espnet.asr.pytorch_backend.asr_init import load_trained_modules
  from espnet.nets.pytorch_backend.nets_utils import pad_list
  from espnet.nets.tts_interface import TTSInterface
  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.evaluator import BaseEvaluator
  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()