code: def test_inference_semantic_segmentation(self):
      image_processor = DPTImageProcessor.from_pretrained(
          "Intel/dpt-large-ade"
      )
      model = DPTForSemanticSegmentation.from_pretrained(
          "Intel/dpt-large-ade"
      ).to(torch_device)
  
      image = prepare_img()
      inputs = image_processor(images=image, return_tensors="pt").to(
          torch_device
      )
  
      # forward pass
      with torch.no_grad():
          outputs = model(**inputs)
  
      # verify the logits
      expected_shape = torch.Size((1, 150, 480, 480))
      self.assertEqual(outputs.logits.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [
              [4.0480, 4.2420, 4.4360],
              [4.3124, 4.5693, 4.8261],
              [4.5768, 4.8965, 5.2163],
          ]
      ).to(torch_device)
  
      self.assertTrue(
          torch.allclose(
              outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4
          )
      )
  
imports: import unittest
  from transformers import DPTConfig
  from transformers.file_utils import 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 ...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 2 for calling the DPTModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_semantic_segmentation() without return types