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.
docstr: Load the masked LM dictionary from the filename
  
  Args:
      filename (str): the filename
code: def load_dictionary(cls, filename):
      """Load the masked LM dictionary from the filename
  
      Args:
          filename (str): the filename
      """
      return MaskedLMDictionary.load(filename)
  
file_path: fairseq/fairseq/tasks/translation_from_pretrained_xlm.py
imports: from dataclasses import dataclass
  from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary
  from fairseq.tasks.translation import TranslationConfig, TranslationTask
  from .None import register_task
label_desc: Use image node_id 1 for calling the TranslationFromPretrainedXLMTask obj's underlying member method code with example usage: obj.load_dictionary(cls, filename) and returns: MaskedLMDictionary