code: def test_classification_forward(self):
      model = VinVLForClassification().to(get_current_device())
      model.eval()
  
      with torch.no_grad():
          model_output = model(
              input_ids=self.input_ids,
              img_feats=self.img_feats,
              attention_mask=self.attention_mask,
              token_type_ids=self.token_type_ids,
              labels=self.labels,
          )
      self.assertTrue("losses" in model_output)
      self.assertTrue("scores" in model_output)
      self.assertTrue("ce" 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 2 for calling the TestVinVLForClassificationAndPretraining obj's underlying member method code with example usage: obj.test_classification_forward() without return types