code: def test_inference_visual_question_answering(self):
      model = ViltForQuestionAnswering.from_pretrained(
          "dandelin/vilt-b32-finetuned-vqa"
      ).to(torch_device)
  
      processor = self.default_processor
      image = prepare_img()
      text = "How many cats are there?"
      inputs = processor(image, text, return_tensors="pt").to(
          torch_device
      )
  
      # forward pass
      with torch.no_grad():
          outputs = model(**inputs)
  
      # verify the logits
      expected_shape = torch.Size((1, 3129))
      self.assertEqual(outputs.logits.shape, expected_shape)
  
      expected_slice = torch.tensor([-15.9495, -18.1472, -10.3041]).to(
          torch_device
      )
  
      self.assertTrue(
          torch.allclose(
              outputs.logits[0, :3], expected_slice, atol=1e-4
          )
      )
  
      # compute loss
      vqa_labels = [[2, 3, 155, 800]]
      vqa_scores = [[1.0, 0.3, 0.3, 0.3]]
      labels = torch.zeros(1, model.config.num_labels).to(torch_device)
  
      for i, (labels_example, scores_example) in enumerate(
          zip(vqa_labels, vqa_scores)
      ):
          for l, s in zip(labels_example, scores_example):
              labels[i, l] = s
  
      # forward pass
      outputs = model(**inputs, labels=labels)
  
      # verify we have a positive loss
      self.assertTrue(outputs.loss > 0)
  
imports: import unittest
  from datasets import load_dataset
  from packaging import version
  from transformers import ViltConfig, is_torch_available, is_vision_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 3 for calling the ViltModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_visual_question_answering() without return types