class_docstr: E2E module.
  
  Args:
      idim (int): Input dimmensions.
      odim (int): Output dimmensions.
      args (Namespace): Training config.
      ignore_id (int, optional): Id for ignoring a character.
      flag_return (bool, optional): If true, return a list with (loss,
      loss_ctc, loss_att, acc) in forward. Otherwise, return loss.
class_docstr_tok: ["E2E","module",".","Args",":","idim","(","int",")",":","Input","dimmensions",".","odim","(","int",")",":","Output","dimmensions",".","args","(","Namespace",")",":","Training","config",".","ignore_id","(","int",",","optional",")",":","Id","for","ignoring","a","character",".","flag_return","(","bool",",","optional",")",":","If","true",",","return","a","list","with","(","loss",",","loss_ctc",",","loss_att",",","acc",")","in","forward",".","Otherwise",",","return","loss","."]
docstr: Customize flags for transformer setup.
  
  Args:
      parser (Namespace): Training config.
docstr_tok: ["Customize","flags","for","transformer","setup",".","Args",":","parser","(","Namespace",")",":","Training","config","."]
code: def add_arguments(parser):
      """Customize flags for transformer setup.
  
      Args:
          parser (Namespace): Training config.
  
      """
      group = parser.add_argument_group("transformer model setting")
      group.add_argument(
          "--transformer-init",
          type=str,
          default="pytorch",
          help="how to initialize transformer parameters",
      )
      group.add_argument(
          "--transformer-input-layer",
          type=str,
          default="conv2d",
          choices=["conv2d", "linear", "embed"],
          help="transformer input layer type",
      )
      group.add_argument(
          "--transformer-attn-dropout-rate",
          default=None,
          type=float,
          help="dropout in transformer attention. use --dropout-rate if None is set",
      )
      group.add_argument(
          "--transformer-lr",
          default=10.0,
          type=float,
          help="Initial value of learning rate",
      )
      group.add_argument(
          "--transformer-warmup-steps",
          default=25000,
          type=int,
          help="optimizer warmup steps",
      )
      group.add_argument(
          "--transformer-length-normalized-loss",
          default=True,
          type=strtobool,
          help="normalize loss by length",
      )
  
      group.add_argument(
          "--dropout-rate",
          default=0.0,
          type=float,
          help="Dropout rate for the encoder",
      )
      # Encoder
      group.add_argument(
          "--elayers",
          default=4,
          type=int,
          help="Number of encoder layers (for shared recognition part "
          "in multi-speaker asr mode)",
      )
      group.add_argument(
          "--eunits",
          "-u",
          default=300,
          type=int,
          help="Number of encoder hidden units",
      )
      # Attention
      group.add_argument(
          "--adim",
          default=320,
          type=int,
          help="Number of attention transformation dimensions",
      )
      group.add_argument(
          "--aheads",
          default=4,
          type=int,
          help="Number of heads for multi head attention",
      )
      # Decoder
      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",
      )
      return parser
  
imports: import logging
  import math
  from argparse import Namespace
  from distutils.util import strtobool
  import chainer
  import chainer.functions
  import numpy
  from chainer import reporter
  from espnet.nets.chainer_backend.asr_interface import ChainerASRInterface
  from espnet.nets.chainer_backend.transformer import ctc
  from espnet.nets.chainer_backend.transformer.attention import MultiHeadAttention
  from espnet.nets.chainer_backend.transformer.decoder import Decoder
  from espnet.nets.chainer_backend.transformer.encoder import Encoder
  from espnet.nets.chainer_backend.transformer.label_smoothing_loss import LabelSmoothingLoss
  from espnet.nets.chainer_backend.transformer.training import CustomConverter, CustomParallelUpdater, CustomUpdater
  from espnet.nets.ctc_prefix_score import CTCPrefixScore
  from espnet.nets.e2e_asr_common import ErrorCalculator, end_detect
  from espnet.nets.pytorch_backend.nets_utils import get_subsample
  from espnet.nets.pytorch_backend.transformer.plot import PlotAttentionReport
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: parser