code: def perform_certification_test(
      base_classifier: tf.keras.Model,
      dataset: (np.ndarray, np.ndarray),
      sigma: float,
      outfile: str = "certification_output/sigma_0.25",
  ) -> None:
      """Performs a certification test.
  
      :param base_classifier: classification model
      :param dataset: dataset consisting of (images, labels)
      :param sigma: the noise level hyperparameter
      :param outfile: output file to serialize the outputs to
      :return: None
      """
      smoothed_classifier = certification_utils.Smooth(
          base_classifier, 10, sigma
      )
      if not os.path.exists(outfile.split("sigma")[0]):
          os.makedirs(outfile.split("sigma")[0])
  
      with open(outfile, "w") as f:
          print(
              "idx\tlabel\tpredict\tradius\tcorrect\ttime",
              file=f,
              flush=True,
          )
      print("idx\tlabel\tpredict\tradius\tcorrect\ttime", flush=True)
  
      images, labels = dataset
      for i in range(len(images)):
          if i % SKIP != 0:
              continue
          if i == MAX:
              break
  
          (x, label) = images[i], labels[i]
  
          before_time = time()
          # certify the prediction of g around x
          prediction, radius = smoothed_classifier.certify(
              x, 100, 10000, 0.001, 1000
          )
          after_time = time()
          correct = int(prediction == label)
  
          time_elapsed = str(
              datetime.timedelta(seconds=(after_time - before_time))
          )
  
          with open(outfile, "a") as f:
              print(
                  "{}\t{}\t{}\t{:.3}\t{}\t{}".format(
                      i,
                      label,
                      prediction,
                      radius,
                      correct,
                      time_elapsed,
                  ),
                  file=f,
                  flush=True,
              )
          print(
              "{}\t{}\t{}\t{:.3}\t{}\t{}".format(
                  i, label, prediction, radius, correct, time_elapsed
              ),
              flush=True,
          )
  
imports: from time import time
  import datetime
  import os
  import tensorflow
  import numpy
  from .None import certification_utils
label_desc: Use image node_id 1 for calling a global function with example usage: perform_certification_test(base_classifier, dataset, sigma, outfile) without return types