code: def test_different_facial_recognition_models():
      dataset = [
          ["dataset/img1.jpg", "dataset/img2.jpg", True],
          ["dataset/img5.jpg", "dataset/img6.jpg", True],
          ["dataset/img6.jpg", "dataset/img7.jpg", True],
          ["dataset/img8.jpg", "dataset/img9.jpg", True],
          ["dataset/img1.jpg", "dataset/img11.jpg", True],
          ["dataset/img2.jpg", "dataset/img11.jpg", True],
          ["dataset/img1.jpg", "dataset/img3.jpg", False],
          ["dataset/img2.jpg", "dataset/img3.jpg", False],
          ["dataset/img6.jpg", "dataset/img8.jpg", False],
          ["dataset/img6.jpg", "dataset/img9.jpg", False],
      ]
  
      expected_coverage = 97.53  # human level accuracy on LFW
      successful_tests = 0
      unsuccessful_tests = 0
      for model in models:
          for metric in metrics:
              for instance in dataset:
                  img1 = instance[0]
                  img2 = instance[1]
                  result = instance[2]
  
                  resp_obj = DeepFace.verify(
                      img1,
                      img2,
                      model_name=model,
                      distance_metric=metric,
                  )
  
                  prediction = resp_obj["verified"]
                  distance = round(resp_obj["distance"], 2)
                  threshold = resp_obj["threshold"]
  
                  if prediction is result:
                      test_result_label = "✅"
                      successful_tests += 1
                  else:
                      test_result_label = "❌"
                      unsuccessful_tests += 1
  
                  if prediction is True:
                      classified_label = "same person"
                  else:
                      classified_label = "different persons"
  
                  img1_alias = img1.split("/", maxsplit=1)[-1]
                  img2_alias = img2.split("/", maxsplit=1)[-1]
  
                  logger.debug(
                      f"{test_result_label} Pair {img1_alias}-{img2_alias}"
                      f" is {classified_label} based on {model}-{metric}"
                      f" (Distance: {distance}, Threshold: {threshold})",
                  )
  
      coverage_score = (100 * successful_tests) / (
          successful_tests + unsuccessful_tests
      )
      assert (
          coverage_score > expected_coverage
      ), f"⛔ facial recognition models test failed with {coverage_score} score"
  
      logger.info(
          f"✅ facial recognition models test passed with {coverage_score}"
      )
  
imports: import cv2
  from deepface import DeepFace
  from deepface.commons.logger import Logger
label_desc: Use image node_id 1 for calling a global function with example usage: test_different_facial_recognition_models() without return types