class_docstr: E2E module for chainer backend.
  
  Args:
      idim (int): Dimension of the inputs.
      odim (int): Dimension of the outputs.
      args (parser.args): Training config.
      flag_return (bool): If True, train() would return
          additional metrics in addition to the training
          loss.
docstr: Add arguments.
docstr_tok: ["Add","arguments","."]
code: def add_arguments(parser):
      """Add arguments."""
      return E2E_pytorch.add_arguments(parser)
  
imports: import logging
  import math
  import chainer
  import numpy
  from chainer import reporter
  from espnet.nets.chainer_backend.asr_interface import ChainerASRInterface
  from espnet.nets.chainer_backend.ctc import ctc_for
  from espnet.nets.chainer_backend.rnn.attentions import att_for
  from espnet.nets.chainer_backend.rnn.decoders import decoder_for
  from espnet.nets.chainer_backend.rnn.encoders import encoder_for
  from espnet.nets.e2e_asr_common import label_smoothing_dist
  from espnet.nets.pytorch_backend.e2e_asr import E2E
  from espnet.nets.pytorch_backend.nets_utils import get_subsample
label_desc: Use image node_id 1 for calling the E2E obj's underlying member method code with example usage: obj.add_arguments(parser) and returns: E2E_pytorch