code: def __call__(self, item: Dict[str, Any]):
      if "text" in item:
          text_a = item["text"]
      elif "text_a" in item:
          text_a = item["text_a"]
      else:
          text_a = " ".join(item["tokens"])
  
      if isinstance(text_a, list):
          text_a = " ".join(text_a)
  
      tokens_a = self.tokenize(text_a)
  
      # 'text_b' can be defined in the dataset preparation
      tokens_b = None
      if "text_b" in item:
          text_b = item["text_b"]
          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["text"] = output["tokens"]
      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 2 for calling the BertTokenizer obj's underlying member method code with example usage: obj.__call__(item) and returns: output