code: def __init__(self, args, dicts, training):
      super().__init__(args, dicts, training)
      self.lang_pairs = [f"{self.source_lang}-{self.target_lang}"]
      if getattr(self.args, "denoising_source_parallel", False):
          self.lang_pairs.append(
              f"{self.source_lang}-{self.source_lang}"
          )
      if getattr(self.args, "denoising_target_parallel", False):
          self.lang_pairs.append(
              f"{self.target_lang}-{self.target_lang}"
          )
      if getattr(self.args, "denoising_source_mono", False):
          self.lang_pairs.append(
              f"{self.source_lang}-{self.source_lang}_"
              f"{constants.MONOLINGUAL_DATA_IDENTIFIER}"
          )
      if getattr(self.args, "denoising_target_mono", False):
          self.lang_pairs.append(
              f"{self.target_lang}-{self.target_lang}_"
              f"{constants.MONOLINGUAL_DATA_IDENTIFIER}"
          )
  
      self.eval_lang_pairs = [f"{self.source_lang}-{self.target_lang}"]
      self.model_lang_pairs = [f"{self.source_lang}-{self.target_lang}"]
      # This is explicitly set so that we can re-use code from
      # MultilingualTranslationTask
      self.args.lang_pairs = self.lang_pairs
  
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 1 to create a new PytorchTranslateDenoisingAutoencoder object from inherited base classes: PytorchTranslateSemiSupervised with example: obj = PytorchTranslateDenoisingAutoencoder(args, dicts, training)