class_docstr: E2E module.
  
  :param int idim: dimension of inputs
  :param int odim: dimension of outputs
  :param Namespace args: argument Namespace containing options
class_docstr_tok: ["E2E","module",".",":","param","int","idim",":","dimension","of","inputs",":","param","int","odim",":","dimension","of","outputs",":","param","Namespace","args",":","argument","Namespace","containing","options"]
docstr: Add arguments for the decoder.
docstr_tok: ["Add","arguments","for","the","decoder","."]
code: def decoder_add_arguments(parser):
      """Add arguments for the decoder."""
      group = parser.add_argument_group("E2E decoder setting")
      group = add_arguments_rnn_decoder_common(group)
      return parser
  
imports: import argparse
  import logging
  import math
  import os
  import chainer
  import nltk
  import numpy
  import torch
  from chainer import reporter
  from espnet.nets.e2e_asr_common import label_smoothing_dist
  from espnet.nets.mt_interface import MTInterface
  from espnet.nets.pytorch_backend.initialization import uniform_init_parameters
  from espnet.nets.pytorch_backend.nets_utils import get_subsample, pad_list, to_device
  from espnet.nets.pytorch_backend.rnn.argument import add_arguments_rnn_attention_common, add_arguments_rnn_decoder_common, add_arguments_rnn_encoder_common
  from espnet.nets.pytorch_backend.rnn.attentions import att_for
  from espnet.nets.pytorch_backend.rnn.decoders import decoder_for
  from espnet.nets.pytorch_backend.rnn.encoders import encoder_for
  from espnet.utils.fill_missing_args import fill_missing_args
label_desc: Use image node_id 4 for calling the E2E obj's underlying member method code with example usage: obj.decoder_add_arguments(parser) and returns: parser