code: def test_pretraining_forward(self):
      model = VinVLForPretraining().to(get_current_device())
      model.eval()
  
      with torch.no_grad():
          model_output = model(
              img_feats=self.img_feats,
              attention_mask=self.attention_mask,
              token_type_ids=self.token_type_ids,
              input_ids_masked=self.input_ids,
              lm_label_ids=self.lm_label_ids,
              contrastive_labels=self.contrastive_labels,
              input_ids_corrupt=self.input_ids,
              token_type_ids_corrupt=self.token_type_ids,
              attention_mask_corrupt=self.attention_mask,
          )
      self.assertTrue("losses" in model_output)
      self.assertTrue("masked_lm_loss" in model_output["losses"])
      self.assertTrue(
          "three_way_contrastive_loss" in model_output["losses"]
      )
  
imports: import unittest
  import tests.test_utils
  import torch
  from mmf.common.sample import SampleList
  from mmf.models.vinvl import VinVLBase, VinVLForClassification, VinVLForPretraining
  from mmf.utils.build import build_model
  from mmf.utils.configuration import Configuration
  from mmf.utils.env import setup_imports, teardown_imports
  from mmf.utils.general import get_current_device
  from omegaconf import OmegaConf
label_desc: Use image node_id 3 for calling the TestVinVLForClassificationAndPretraining obj's underlying member method code with example usage: obj.test_pretraining_forward() without return types