code: def test_inference_depth_estimation(self):
      feature_extractor = DPTFeatureExtractor.from_pretrained(
          "Intel/dpt-large"
      )
      model = DPTForDepthEstimation.from_pretrained(
          "Intel/dpt-large"
      ).to(torch_device)
  
      image = prepare_img()
      inputs = feature_extractor(images=image, return_tensors="pt").to(
          torch_device
      )
  
      # forward pass
      with torch.no_grad():
          outputs = model(**inputs)
          predicted_depth = outputs.predicted_depth
  
      # verify the predicted depth
      expected_shape = torch.Size((1, 384, 384))
      self.assertEqual(predicted_depth.shape, expected_shape)
  
      expected_slice = torch.tensor(
          [
              [6.3199, 6.3629, 6.4148],
              [6.3850, 6.3615, 6.4166],
              [6.3519, 6.3176, 6.3575],
          ]
      ).to(torch_device)
  
      self.assertTrue(
          torch.allclose(
              outputs.predicted_depth[0, :3, :3],
              expected_slice,
              atol=1e-4,
          )
      )
  
imports: import inspect
  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, floats_tensor, ids_tensor
label_desc: Use image node_id 1 for calling the DPTModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_depth_estimation() without return types