class_docstr: Implement the defensive distillation mechanism.
  
  | Paper link: https://arxiv.org/abs/1511.04508
docstr: Create an instance of the defensive distillation defence.
  
  :param classifier: A trained classifier.
  :param batch_size: Size of batches.
  :param nb_epochs: Number of epochs to use for training.
code: def __init__(
      self,
      classifier: "CLASSIFIER_TYPE",
      batch_size: int = 128,
      nb_epochs: int = 10,
  ) -> None:
      """
      Create an instance of the defensive distillation defence.
  
      :param classifier: A trained classifier.
      :param batch_size: Size of batches.
      :param nb_epochs: Number of epochs to use for training.
      """
      super().__init__(classifier=classifier)
      self._is_fitted = True
      self.batch_size = batch_size
      self.nb_epochs = nb_epochs
      self._check_params()
  
file_path: adversarial-robustness-toolbox/art/defences/transformer/evasion/defensive_distillation.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, TYPE_CHECKING
  import numpy
  from art.defences.transformer.transformer import Transformer
  from art.utils import is_probability
label_desc: Use image node_id 1 to create a new DefensiveDistillation object from inherited base classes: Transformer with example: obj = DefensiveDistillation(classifier, batch_size, nb_epochs)