class_docstr: Utility for training shadow models and generating shadow-datasets for membership inference attacks in scikit-learn,
  PyTorch and TensorFlow v2.
docstr: Returns the list of shadow models. `generate_shadow_dataset` or `generate_synthetic_shadow_dataset` must be
  called for the shadow models to be trained.
docstr_tok: ["Returns","the","list","of","shadow","models",".","`","generate_shadow_dataset","`","or","`","generate_synthetic_shadow_dataset","`","must","be","called","for","the","shadow","models","to","be","trained","."]
code: def get_shadow_models(
      self,
  ) -> Sequence["CLONABLE"]:
      """
      Returns the list of shadow models. `generate_shadow_dataset` or `generate_synthetic_shadow_dataset` must be
      called for the shadow models to be trained.
      """
      return self._shadow_models
  
file_path: adversarial-robustness-toolbox/art/attacks/inference/membership_inference/shadow_models.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import math
  from functools import reduce
  from typing import Callable, Tuple, TYPE_CHECKING, List, Optional, Sequence
  import numpy
label_desc: Use image node_id 7 for calling the ShadowModels obj's underlying member method code with example usage: obj.get_shadow_models() and returns: self