code: def run_pipeline_test(self, image_classifier, examples):
      outputs = image_classifier(
          "./tests/fixtures/tests_samples/COCO/000000039769.png"
      )
  
      self.assertEqual(
          outputs,
          [
              {"score": ANY(float), "label": ANY(str)},
              {"score": ANY(float), "label": ANY(str)},
          ],
      )
  
      import datasets
  
      dataset = datasets.load_dataset(
          "hf-internal-testing/fixtures_image_utils",
          "image",
          split="test",
      )
  
      # Accepts URL + PIL.Image + lists
      outputs = image_classifier(
          [
              Image.open(
                  "./tests/fixtures/tests_samples/COCO/000000039769.png"
              ),
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              # RGBA
              dataset[0]["file"],
              # LA
              dataset[1]["file"],
              # L
              dataset[2]["file"],
          ]
      )
      self.assertEqual(
          outputs,
          [
              [
                  {"score": ANY(float), "label": ANY(str)},
                  {"score": ANY(float), "label": ANY(str)},
              ],
              [
                  {"score": ANY(float), "label": ANY(str)},
                  {"score": ANY(float), "label": ANY(str)},
              ],
              [
                  {"score": ANY(float), "label": ANY(str)},
                  {"score": ANY(float), "label": ANY(str)},
              ],
              [
                  {"score": ANY(float), "label": ANY(str)},
                  {"score": ANY(float), "label": ANY(str)},
              ],
              [
                  {"score": ANY(float), "label": ANY(str)},
                  {"score": ANY(float), "label": ANY(str)},
              ],
          ],
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/tests/pipelines/test_pipelines_image_classification.py
imports: import unittest
  from transformers import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, PreTrainedTokenizer, is_vision_available
  from transformers.pipelines import ImageClassificationPipeline, pipeline
  from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_torch_or_tf, require_vision, slow
  from .test_pipelines_common import ANY, PipelineTestCaseMeta
label_desc: Use image node_id 2 for calling the ImageClassificationPipelineTests obj's underlying member method code with example usage: obj.run_pipeline_test(image_classifier, examples) without return types