code: def cross_entropy(prob1, prob2, eps=1e-10):
      """
      Compute cross-entropy between two probability distributions.
  
      :param prob1: First probability distribution.
      :type prob1: `np.ndarray`
      :param prob2: Second probability distribution.
      :type prob2: `np.ndarray`
      :param eps: A small amount to avoid the possibility of having a log of zero.
      :type eps: `float`
      :return: Cross entropy.
      :rtype: `float`
      """
      prob1 = np.clip(prob1, eps, 1.0 - eps)
      size = prob1.shape[0]
      result = -np.sum(prob2 * np.log(prob1 + eps)) / size
  
      return result
  
file_path: adversarial-robustness-toolbox/tests/defences/test_defensive_distillation.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import unittest
  import numpy
  from art.defences.transformer.evasion import DefensiveDistillation
  from tests.utils import master_seed, TestBase
  from tests.utils import get_image_classifier_tf, get_image_classifier_pt, get_image_classifier_kr
  from tests.utils import get_tabular_classifier_tf, get_tabular_classifier_kr, get_tabular_classifier_pt
label_desc: Use image node_id 1 for calling a global function with example usage: cross_entropy(prob1, prob2, eps) and returns: result