code: def clever_u(
      classifier: "CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE",
      x: np.ndarray,
      nb_batches: int,
      batch_size: int,
      radius: float,
      norm: int,
      c_init: float = 1.0,
      pool_factor: int = 10,
      verbose: bool = True,
  ) -> float:
      """
      Compute CLEVER score for an untargeted attack.
  
      | Paper link: https://arxiv.org/abs/1801.10578
  
      :param classifier: A trained model.
      :param x: One input sample.
      :param nb_batches: Number of repetitions of the estimate.
      :param batch_size: Number of random examples to sample per batch.
      :param radius: Radius of the maximum perturbation.
      :param norm: Current support: 1, 2, np.inf.
      :param c_init: initialization of Weibull distribution.
      :param pool_factor: The factor to create a pool of random samples with size pool_factor x n_s.
      :param verbose: Show progress bars.
      :return: CLEVER score.
      """
      # Get a list of untargeted classes
      y_pred = classifier.predict(np.array([x]))
      pred_class = np.argmax(y_pred, axis=1)[0]
      untarget_classes = [
          i for i in range(classifier.nb_classes) if i != pred_class
      ]
  
      # Compute CLEVER score for each untargeted class
      score_list = []
      for j in tqdm(
          untarget_classes,
          desc="CLEVER untargeted",
          disable=not verbose,
      ):
          score = clever_t(
              classifier,
              x,
              j,
              nb_batches,
              batch_size,
              radius,
              norm,
              c_init,
              pool_factor,
          )
          score_list.append(score)
  
      return np.min(score_list)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from functools import reduce
  import logging
  from typing import Any, Dict, List, Optional, Union, TYPE_CHECKING
  import numpy
  import numpy.linalg
  from scipy.optimize import fmin
  from scipy.stats import weibull_min
  from tqdm.auto import tqdm
  from art.config import ART_NUMPY_DTYPE
  from art.attacks.attack import EvasionAttack
  from art.attacks.evasion.auto_attack import AutoAttack
  from art.attacks.evasion.fast_gradient import FastGradientMethod
  from art.attacks.evasion.hop_skip_jump import HopSkipJump
  from art.utils import random_sphere
label_desc: Use image node_id 6 for calling a global function with example usage: clever_u(classifier, x, nb_batches, batch_size, radius, norm, c_init, pool_factor, verbose) and returns: np