code: def get_test_pipeline(self, model, tokenizer, feature_extractor):
      image_classifier = ImageClassificationPipeline(
          model=model, feature_extractor=feature_extractor, top_k=2
      )
      examples = [
          Image.open(
              "./tests/fixtures/tests_samples/COCO/000000039769.png"
          ),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
      ]
      return image_classifier, examples
  
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 1 for calling the ImageClassificationPipelineTests obj's underlying member method code with example usage: obj.get_test_pipeline(model, tokenizer, feature_extractor) and returns: image_classifier,  examples