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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def random_search_max_confidence_recipe(sess, model, x, y, eps, clip_min, clip_max, report_path, batch_size=BATCH_SIZE, num_noise_points=10000):
 """Max confidence using random search. References: https://openreview.net/forum?id=H1g0piA9tQ Describes the max_confidence procedure used for the bundling in this recipe https://arxiv.org/abs/1802.00420 Describes using random search with 1e5 or more random points to avoid gradient masking. :param sess: tf.Session :param model: cleverhans.model.Model :param x: numpy array containing clean example inputs to attack :param y: numpy array containing true labels :param nb_classes: int, number of classes :param eps: float, maximum size of perturbation (measured by max norm) :param eps_iter: float, step size for one version of PGD attacks (will also run another version with 25X smaller step size) :param nb_iter: int, number of iterations for one version of PGD attacks (will also run another version with 25X more iterations) :param report_path: str, the path that the report will be saved to. :batch_size: int, the total number of examples to run simultaneously """

  noise_attack = Noise(model, sess)
  threat_params = {"eps": eps, "clip_min": clip_min, "clip_max": clip_max}
  noise_attack_config = AttackConfig(noise_attack, threat_params)
  attack_configs = [noise_attack_config]
  assert batch_size % num_devices == 0
  new_work_goal = {noise_attack_config: num_noise_points}
  goals = [MaxConfidence(t=1., new_work_goal=new_work_goal)]
  bundle_attacks(sess, model, x, y, attack_configs, goals, report_path)