code: def test_inference_vcr(self):
      model = VisualBertForMultipleChoice.from_pretrained(
          "uclanlp/visualbert-vcr"
      )
  
      input_ids = torch.tensor(
          [[[1, 2, 3, 4, 5, 6] for i in range(4)]], dtype=torch.long
      )
      attention_mask = torch.ones_like(input_ids)
      token_type_ids = torch.ones_like(input_ids)
  
      visual_embeds = (
          torch.ones(size=(1, 4, 10, 512), dtype=torch.float32) * 0.5
      )
      visual_token_type_ids = torch.ones(
          size=(1, 4, 10), dtype=torch.long
      )
      visual_attention_mask = torch.ones_like(visual_token_type_ids)
  
      with torch.no_grad():
          output = model(
              input_ids=input_ids,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              visual_embeds=visual_embeds,
              visual_attention_mask=visual_attention_mask,
              visual_token_type_ids=visual_token_type_ids,
          )
  
      # vocab_size = 30522
  
      expected_shape = torch.Size((1, 4))
      self.assertEqual(output.logits.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [[-7.7697, -7.7697, -7.7697, -7.7697]]
      )
  
      self.assertTrue(
          torch.allclose(output.logits, expected_slice, atol=1e-4)
      )
  
imports: import copy
  import unittest
  from transformers import VisualBertConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
label_desc: Use image node_id 4 for calling the VisualBertModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_vcr() without return types