code: def test_inference(self):
      processor = VitMatteImageProcessor.from_pretrained(
          "hustvl/vitmatte-small-composition-1k"
      )
      model = VitMatteForImageMatting.from_pretrained(
          "hustvl/vitmatte-small-composition-1k"
      ).to(torch_device)
  
      filepath = hf_hub_download(
          repo_id="hf-internal-testing/image-matting-fixtures",
          filename="image.png",
          repo_type="dataset",
      )
      image = Image.open(filepath).convert("RGB")
      filepath = hf_hub_download(
          repo_id="hf-internal-testing/image-matting-fixtures",
          filename="trimap.png",
          repo_type="dataset",
      )
      trimap = Image.open(filepath).convert("L")
  
      # prepare image + trimap for the model
      inputs = processor(
          images=image, trimaps=trimap, return_tensors="pt"
      ).to(torch_device)
  
      with torch.no_grad():
          alphas = model(**inputs).alphas
  
      expected_shape = torch.Size((1, 1, 640, 960))
      self.assertEqual(alphas.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [
              [0.9977, 0.9987, 0.9990],
              [0.9980, 0.9998, 0.9998],
              [0.9983, 0.9998, 0.9998],
          ],
          device=torch_device,
      )
      self.assertTrue(
          torch.allclose(
              alphas[0, 0, :3, :3], expected_slice, atol=1e-4
          )
      )
  
imports: import unittest
  from huggingface_hub import hf_hub_download
  from transformers import VitMatteConfig
  from transformers.testing_utils import require_torch, slow, torch_device
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the VitMatteModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference() without return types