code: def test_inference_natural_language_visual_reasoning(self):
      model = ViltForImagesAndTextClassification.from_pretrained(
          "dandelin/vilt-b32-finetuned-nlvr2"
      ).to(torch_device)
  
      processor = self.default_processor
  
      dataset = load_dataset(
          "hf-internal-testing/fixtures_nlvr2", split="test"
      )
      image1 = Image.open(dataset[0]["file"]).convert("RGB")
      image2 = Image.open(dataset[1]["file"]).convert("RGB")
  
      text = (
          "The left image contains twice the number of dogs as the right image, and at least two dogs in total are"
          " standing."
      )
      encoding_1 = processor(image1, text, return_tensors="pt")
      encoding_2 = processor(image2, text, return_tensors="pt")
  
      pixel_values = torch.stack(
          [encoding_1.pixel_values, encoding_2.pixel_values], dim=1
      )
  
      # forward pass
      outputs = model(
          input_ids=encoding_1.input_ids.to(torch_device),
          pixel_values=pixel_values.to(torch_device),
      )
  
      # verify the logits
      expected_shape = torch.Size([1, 2])
      self.assertEqual(outputs.logits.shape, expected_shape)
  
      is_pillow_less_than_9 = version.parse(
          PIL.__version__
      ) < version.parse("9.0.0")
  
      if is_pillow_less_than_9:
          expected_slice = torch.tensor(
              [-2.4013, 2.9342],
              device=torch_device,
          )
      else:
          expected_slice = torch.tensor(
              [-2.3713, 2.9168],
              device=torch_device,
          )
  
      self.assertTrue(
          torch.allclose(
              outputs.logits[0, :3], expected_slice, atol=1e-4
          )
      )
  
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 4 for calling the ViltModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_natural_language_visual_reasoning() without return types