code: def test_small_model_tf(self):
      small_model = "hf-internal-testing/tiny-random-vit"
      image_classifier = pipeline(
          "image-classification", model=small_model, framework="tf"
      )
  
      outputs = image_classifier(
          "http://images.cocodataset.org/val2017/000000039769.jpg"
      )
      self.assertEqual(
          nested_simplify(outputs, decimals=4),
          [
              {"label": "LABEL_1", "score": 0.574},
              {"label": "LABEL_0", "score": 0.426},
          ],
      )
  
      outputs = image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ],
          top_k=2,
      )
      self.assertEqual(
          nested_simplify(outputs, decimals=4),
          [
              [
                  {"label": "LABEL_1", "score": 0.574},
                  {"label": "LABEL_0", "score": 0.426},
              ],
              [
                  {"label": "LABEL_1", "score": 0.574},
                  {"label": "LABEL_0", "score": 0.426},
              ],
          ],
      )
  
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 4 for calling the ImageClassificationPipelineTests obj's underlying member method code with example usage: obj.test_small_model_tf() without return types