code: def _get_contrastive_output(
      self, item: Dict[str, Any]
  ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
      error_msg = (
          "'{}' are required in the annotations for VinVL pretraining."
          + "These should be created using the MMF feature extraction script for MMF."
          + "To run finetuning or pretraining without contrastive loss,"
          + " set corrupt_probability to 0."
      )
      assert "random_captions" in item, error_msg.format(
          "random_captions"
      )
      assert "random_labels" in item, error_msg.format("random_labels")
      assert "text_b" in item, error_msg.format("text_b")
  
      corrupt_item = copy.deepcopy(item)
      p_match = 1 - self._corrupt_prob
      p_caption = self._corrupt_prob * self._corrupt_caption_prob
      p_label = self._corrupt_prob * (1 - self._corrupt_caption_prob)
      contrastive_label = torch.multinomial(
          torch.tensor([p_match, p_caption, p_label]), num_samples=1
      ).long()
      if contrastive_label == 0:
          pass
      elif contrastive_label == 1:
          num_subs = len(item["random_captions"])
          neg_index = torch.randint(num_subs, (1,))
          corrupt_item["text"] = item["random_captions"][neg_index]
      else:
          num_subs = len(item["random_labels"])
          neg_index = torch.randint(num_subs, (1,))
          corrupt_item["text_b"] = item["random_labels"][neg_index]
  
      return contrastive_label, get_pair_text_tokens(corrupt_item, self)
  
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 3 for calling the VinVLTextTokenizer obj's underlying member method code with example usage: obj._get_contrastive_output(item) and returns: contrastive_label,  get_pair_text_tokens