code: def test_inference_vqa_coco_pre(self):
      model = VisualBertForPreTraining.from_pretrained(
          "uclanlp/visualbert-vqa-coco-pre"
      )
  
      input_ids = torch.tensor(
          [1, 2, 3, 4, 5, 6], dtype=torch.long
      ).reshape(1, -1)
      token_type_ids = torch.tensor(
          [0, 0, 0, 1, 1, 1], dtype=torch.long
      ).reshape(1, -1)
      visual_embeds = (
          torch.ones(size=(1, 10, 2048), dtype=torch.float32) * 0.5
      )
      visual_token_type_ids = torch.ones(size=(1, 10), dtype=torch.long)
      attention_mask = torch.tensor([1] * 6).reshape(1, -1)
      visual_attention_mask = torch.tensor([1] * 10).reshape(1, -1)
  
      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, 16, vocab_size))
      self.assertEqual(output.prediction_logits.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [
              [
                  [-5.1858, -5.1903, -4.9142],
                  [-6.2214, -5.9238, -5.8381],
                  [-6.3027, -5.9939, -5.9297],
              ]
          ]
      )
  
      self.assertTrue(
          torch.allclose(
              output.prediction_logits[:, :3, :3],
              expected_slice,
              atol=1e-4,
          )
      )
  
      expected_shape_2 = torch.Size((1, 2))
      self.assertEqual(
          output.seq_relationship_logits.shape, expected_shape_2
      )
  
      expected_slice_2 = torch.tensor([[0.7393, 0.1754]])
  
      self.assertTrue(
          torch.allclose(
              output.seq_relationship_logits,
              expected_slice_2,
              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
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the VisualBertModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_vqa_coco_pre() without return types