code: def get_masked(self, mlm_probability, tokenizer, do_mask):
      if do_mask:
          masked_input_ids, masked_lm_labels = mlm_mask_tokens(
              inputs=self.input_ids,
              tokenizer=tokenizer,
              mlm_probability=mlm_probability,
          )
          return MaskedBatch(
              masked_input_ids=masked_input_ids,
              input_mask=self.input_mask,
              segment_ids=self.segment_ids,
              masked_lm_labels=masked_lm_labels,
              tokens=self.tokens,
          )
      else:
          # Potentially, the mask_lm_labels should all be -100
          return MaskedBatch(
              masked_input_ids=self.input_ids,
              input_mask=self.input_mask,
              segment_ids=self.segment_ids,
              masked_lm_labels=self.input_ids,
              tokens=self.tokens,
          )
  
imports: import numpy
  import torch
  from dataclasses import dataclass
  from typing import List, Tuple
  from jiant.utils.python.datastructures import ReusableGenerator
  from jiant.tasks.core import Task, TaskTypes, BaseExample, BaseTokenizedExample, BaseDataRow, BatchMixin
  from jiant.tasks.lib.templates.shared import construct_single_input_tokens_and_segment_ids, create_input_set_from_tokens_and_segments
label_desc: Use image node_id 1 for calling the Batch obj's underlying member method code with example usage: obj.get_masked(mlm_probability, tokenizer, do_mask) and returns: MaskedBatch, MaskedBatch