code: def run(model_path, n_iterations, delegate):
      """Run an image classifier benchmark.
  
      Args:
          model_path: Path to the TFLite model.
          n_iterations: Number of iterations to run the benchmark.
          delegate: CPU or GPU delegate for inference.
  
      Returns:
          List of inference times.
      """
      # Initialize the image classifier
      options = image_classifier.ImageClassifierOptions(
          base_options=base_options.BaseOptions(
              model_asset_path=model_path, delegate=delegate
          ),
          max_results=1,
      )
  
      with image_classifier.ImageClassifier.create_from_options(
          options
      ) as classifier:
          mp_image = image.Image.create_from_file(
              benchmark_utils.get_test_data_path(
                  base_vision_benchmark_api.VISION_TEST_DATA_DIR,
                  _IMAGE_FILE,
              )
          )
          inference_times = base_vision_benchmark_api.benchmark_task(
              classifier.classify, mp_image, n_iterations
          )
          return inference_times
  
imports: from mediapipe.python._framework_bindings import image
  from mediapipe.tasks.python.benchmark import benchmark_utils
  from mediapipe.tasks.python.benchmark.vision import benchmark
  from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
  from mediapipe.tasks.python.core import base_options
  from mediapipe.tasks.python.vision import image_classifier
label_desc: Use image node_id 1 for calling a global function with example usage: run(model_path, n_iterations, delegate) and returns: inference_times