code: def run_pipeline_test(self, depth_estimator, examples):
      outputs = depth_estimator(
          "./tests/fixtures/tests_samples/COCO/000000039769.png"
      )
      self.assertEqual(
          {
              "predicted_depth": ANY(torch.Tensor),
              "depth": ANY(Image.Image),
          },
          outputs,
      )
      import datasets
  
      # we use revision="refs/pr/1" until the PR is merged
      # https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1
      dataset = datasets.load_dataset(
          "hf-internal-testing/fixtures_image_utils",
          split="test",
          revision="refs/pr/1",
      )
      outputs = depth_estimator(
          [
              Image.open(
                  "./tests/fixtures/tests_samples/COCO/000000039769.png"
              ),
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              # RGBA
              dataset[0]["image"],
              # LA
              dataset[1]["image"],
              # L
              dataset[2]["image"],
          ]
      )
      self.assertEqual(
          [
              {
                  "predicted_depth": ANY(torch.Tensor),
                  "depth": ANY(Image.Image),
              },
              {
                  "predicted_depth": ANY(torch.Tensor),
                  "depth": ANY(Image.Image),
              },
              {
                  "predicted_depth": ANY(torch.Tensor),
                  "depth": ANY(Image.Image),
              },
              {
                  "predicted_depth": ANY(torch.Tensor),
                  "depth": ANY(Image.Image),
              },
              {
                  "predicted_depth": ANY(torch.Tensor),
                  "depth": ANY(Image.Image),
              },
          ],
          outputs,
      )
  
file_path: transformers/tests/pipelines/test_pipelines_depth_estimation.py
imports: import unittest
  from huggingface_hub.utils import insecure_hashlib
  from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
  from transformers.pipelines import DepthEstimationPipeline, pipeline
  from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_timm, require_torch, require_vision, slow
  from .test_pipelines_common import ANY
label_desc: Use image node_id 2 for calling the DepthEstimationPipelineTests obj's underlying member method code with example usage: obj.run_pipeline_test(depth_estimator, examples) without return types