class_docstr: Implement the defensive distillation mechanism.
  
  | Paper link: https://arxiv.org/abs/1511.04508
docstr: No parameters to learn for this method; do nothing.
code: def fit(
      self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs
  ) -> None:
      """
      No parameters to learn for this method; do nothing.
      """
      pass
  
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 3 for calling the DefensiveDistillation obj's underlying member method code with example usage: obj.fit(x, y) without return types