code: def test_inference_semantic_segmentation(self):
      model = BeitForSemanticSegmentation.from_pretrained(
          "microsoft/beit-base-finetuned-ade-640-640"
      )
      model = model.to(torch_device)
  
      image_processor = BeitImageProcessor(
          do_resize=True, size=640, do_center_crop=False
      )
  
      ds = load_dataset(
          "hf-internal-testing/fixtures_ade20k", split="test"
      )
      image = Image.open(ds[0]["file"])
      inputs = image_processor(images=image, return_tensors="pt").to(
          torch_device
      )
  
      # forward pass
      with torch.no_grad():
          outputs = model(**inputs)
      logits = outputs.logits
  
      # verify the logits
      expected_shape = torch.Size((1, 150, 160, 160))
      self.assertEqual(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(
              [
                  [
                      [-4.9225, -2.3954, -3.0522],
                      [-2.8822, -1.0046, -1.7561],
                      [-2.9549, -1.3228, -2.1347],
                  ],
                  [
                      [-5.8168, -3.4129, -4.0778],
                      [-3.8651, -2.2214, -3.0277],
                      [-3.8356, -2.4643, -3.3535],
                  ],
                  [
                      [-0.0078, 3.9952, 4.0754],
                      [2.9856, 4.6944, 5.0035],
                      [3.2413, 4.7813, 4.9969],
                  ],
              ],
              device=torch_device,
          )
      else:
          expected_slice = torch.tensor(
              [
                  [
                      [-4.8960, -2.3688, -3.0355],
                      [-2.8478, -0.9836, -1.7418],
                      [-2.9449, -1.3332, -2.1456],
                  ],
                  [
                      [-5.8081, -3.4124, -4.1006],
                      [-3.8561, -2.2081, -3.0323],
                      [-3.8365, -2.4601, -3.3669],
                  ],
                  [
                      [-0.0309, 3.9868, 4.0540],
                      [2.9640, 4.6877, 4.9976],
                      [3.2081, 4.7690, 4.9942],
                  ],
              ],
              device=torch_device,
          )
  
      self.assertTrue(
          torch.allclose(
              logits[0, :3, :3, :3], expected_slice, atol=1e-4
          )
      )
  
imports: import unittest
  from datasets import load_dataset
  from packaging import version
  from transformers import BeitConfig
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  from ...test_backbone_common import BackboneTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 5 for calling the BeitModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_semantic_segmentation() without return types