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 maskctc model.
docstr_tok: ["Add","arguments","for","maskctc","model","."]
code: def add_maskctc_arguments(parser):
      """Add arguments for maskctc model."""
      group = parser.add_argument_group("maskctc specific setting")
  
      group.add_argument(
          "--maskctc-use-conformer-encoder",
          default=False,
          type=strtobool,
      )
      group = add_arguments_conformer_common(group)
  
      return parser
  
file_path: espnet/espnet/nets/pytorch_backend/e2e_asr_maskctc.py
imports: import logging
  import math
  from distutils.util import strtobool
  from itertools import groupby
  import numpy
  import torch
  from espnet.nets.pytorch_backend.conformer.argument import add_arguments_conformer_common
  from espnet.nets.pytorch_backend.conformer.encoder import Encoder
  from espnet.nets.pytorch_backend.e2e_asr import CTC_LOSS_THRESHOLD
  from espnet.nets.pytorch_backend.e2e_asr_transformer import E2E
  from espnet.nets.pytorch_backend.maskctc.add_mask_token import mask_uniform
  from espnet.nets.pytorch_backend.maskctc.mask import square_mask
  from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask, th_accuracy
label_desc: Use image node_id 2 for calling the E2E obj's underlying member method code with example usage: obj.add_maskctc_arguments(parser) and returns: parser