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.
docstr_tok: ["Add","arguments","."]
code: def add_arguments(parser):
      """Add arguments."""
      E2EASR.add_arguments(parser)
      E2EASRMIX.encoder_mix_add_arguments(parser)
      return parser
  
imports: import logging
  import math
  from argparse import Namespace
  import numpy
  import torch
  from espnet.nets.asr_interface import ASRInterface
  from espnet.nets.ctc_prefix_score import CTCPrefixScore
  from espnet.nets.e2e_asr_common import end_detect
  from espnet.nets.pytorch_backend.ctc import CTC
  from espnet.nets.pytorch_backend.e2e_asr import CTC_LOSS_THRESHOLD
  from espnet.nets.pytorch_backend.e2e_asr_mix import E2E
  from espnet.nets.pytorch_backend.e2e_asr_mix import PIT
  from espnet.nets.pytorch_backend.e2e_asr_transformer import E2E
  from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask, th_accuracy
  from espnet.nets.pytorch_backend.rnn.decoders import CTC_SCORING_RATIO
  from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
  from espnet.nets.pytorch_backend.transformer.encoder_mix import EncoderMix
  from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask, target_mask
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