code: def add_args(parser):
      PytorchTranslateSemiSupervised.add_args(parser)
  
      """
          Add denoising autoencoder arguments to the parser.
          Monolingual data is only required if you are adding a denoising
          autoencoder objective to using monolingual data. It is possible to
          just add a denoising autoencoder objective using one side (source or
          target) of the parallel dataset.
          """
  
      # TODO(T35539829): implement a Noising registry so we can build a noiser
      # and use the corresponding class to pass noise-type specific args
      parser.add_argument(
          "--max-word-shuffle-distance",
          default=3,
          type=int,
          help="Maximum distance to swap words.",
      )
      parser.add_argument(
          "--word-dropout-prob",
          default=0.2,
          type=float,
          help="Probability for dropping words.",
      )
      parser.add_argument(
          "--word-blanking-prob",
          default=0.2,
          type=float,
          help="Probability for replacing a word with an UNK token",
      )
  
      parser.add_argument(
          "--denoising-source-parallel",
          type=utils.bool_flag,
          nargs="?",
          const=True,
          default=False,
          help="Whether to add a denoising autoencoder objective using "
          "the source side of the parallel data",
      )
      parser.add_argument(
          "--denoising-target-parallel",
          type=utils.bool_flag,
          nargs="?",
          const=True,
          default=False,
          help="Whether to add a denoising autoencoder objective using "
          "the target side of the parallel data",
      )
      parser.add_argument(
          "--denoising-source-mono",
          type=utils.bool_flag,
          nargs="?",
          const=True,
          default=False,
          help="Whether to add a denoising autoencoder objective using "
          "the monolingual source corpus",
      )
      parser.add_argument(
          "--denoising-target-mono",
          type=utils.bool_flag,
          nargs="?",
          const=True,
          default=False,
          help="Whether to add a denoising autoencoder objective using "
          "the monolingual source corpus",
      )
  
file_path: translate/pytorch_translate/tasks/denoising_autoencoder_task.py
imports: from collections import OrderedDict
  from fairseq import models
  from fairseq.data import noising, RoundRobinZipDatasets
  from fairseq.tasks import register_task
  from pytorch_translate import constants, utils
  from pytorch_translate.data import utils, weighted_data
  from pytorch_translate.semi_supervised import SemiSupervisedModel
  from pytorch_translate.tasks.pytorch_translate_task import PytorchTranslateTask
  from pytorch_translate.tasks.semi_supervised_task import PytorchTranslateSemiSupervised
label_desc: Use image node_id 2 for calling the PytorchTranslateDenoisingAutoencoder obj's underlying member method code with example usage: obj.add_args(parser) without return types