code: def __call__(self, item: Dict[str, Any]):
      text_a = item["text_a"]
      text_b = item.get("text_b", None)
  
      tokens_a = self.tokenize(text_a)
      tokens_b = None
  
      if text_b:
          tokens_b = self.tokenize(text_b)
  
      self._truncate_seq_pair(tokens_a, tokens_b, self._max_seq_length)
      output = self._convert_to_indices(
          tokens_a, tokens_b, probability=self._probability
      )
      output["is_correct"] = torch.tensor(
          item.get("is_correct", True), dtype=torch.long
      )
  
      return output
  
imports: import copy
  import random
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  from mmf.common.registry import registry
  from mmf.common.sample import Sample, SampleList
  from mmf.datasets.processors.processors import BaseProcessor
label_desc: Use image node_id 9 for calling the MaskedTokenProcessor obj's underlying member method code with example usage: obj.__call__(item) and returns: output