class_docstr: Same as TranslationTask except use the MaskedLMDictionary class so that
  we can load data that was binarized with the MaskedLMDictionary class.
  
  This task should be used for the entire training pipeline when we want to
  train an NMT model from a pretrained XLM checkpoint: binarizing NMT data,
  training NMT with the pretrained XLM checkpoint, and subsequent evaluation
  of that trained model.
class_docstr_tok: ["Same","as","TranslationTask","except","use","the","MaskedLMDictionary","class","so","that","we","can","load","data","that","was","binarized","with","the","MaskedLMDictionary","class",".","This","task","should","be","used","for","the","entire","training","pipeline","when","we","want","to","train","an","NMT","model","from","a","pretrained","XLM","checkpoint",":","binarizing","NMT","data",",","training","NMT","with","the","pretrained","XLM","checkpoint",",","and","subsequent","evaluation","of","that","trained","model","."]
code: def add_args(parser):
      PytorchTranslateTask.add_args(parser)
      """Add task-specific arguments to the parser."""
      parser.add_argument(
          "--save-only",
          action="store_true",
          help="skip eval and only do save",
      )
  
file_path: translate/pytorch_translate/tasks/translation_from_pretrained_xlm.py
imports: from fairseq import options, tokenizer
  from fairseq.tasks import register_task
  from pytorch_translate import constants
  from pytorch_translate.data.masked_lm_dictionary import MaskedLMDictionary
  from pytorch_translate.tasks.pytorch_translate_task import PytorchTranslateTask
label_desc: Use image node_id 1 for calling the PytorchTranslateTranslationFromPretrainedXLMTask obj's underlying member method code with example usage: obj.add_args(parser) without return types