class_docstr: E2E module.
  
  :param List idims: List of dimensions of inputs
  :param int odim: dimension of outputs
  :param Namespace args: argument Namespace containing options
docstr: Add arguments for decoder in multi-encoder setting.
code: def decoder_add_arguments(parser):
      """Add arguments for decoder in multi-encoder setting."""
      group = parser.add_argument_group("E2E decoder setting")
      group.add_argument(
          "--dtype",
          default="lstm",
          type=str,
          choices=["lstm", "gru"],
          help="Type of decoder network architecture",
      )
      group.add_argument(
          "--dlayers",
          default=1,
          type=int,
          help="Number of decoder layers",
      )
      group.add_argument(
          "--dunits",
          default=320,
          type=int,
          help="Number of decoder hidden units",
      )
      group.add_argument(
          "--dropout-rate-decoder",
          default=0.0,
          type=float,
          help="Dropout rate for the decoder",
      )
      group.add_argument(
          "--sampling-probability",
          default=0.0,
          type=float,
          help="Ratio of predicted labels fed back to decoder",
      )
      group.add_argument(
          "--lsm-type",
          const="",
          default="",
          type=str,
          nargs="?",
          choices=["", "unigram"],
          help="Apply label smoothing with a specified distribution type",
      )
      return parser
  
imports: import argparse
  import logging
  import math
  import os
  from itertools import groupby
  import chainer
  import numpy
  import torch
  from chainer import reporter
  from espnet.nets.asr_interface import ASRInterface
  from espnet.nets.e2e_asr_common import label_smoothing_dist
  from espnet.nets.pytorch_backend.ctc import ctc_for
  from espnet.nets.pytorch_backend.nets_utils import get_subsample, pad_list, to_device, to_torch_tensor
  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, encoder_for
  from espnet.nets.scorers.ctc import CTCPrefixScorer
  from espnet.utils.cli_utils import strtobool
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