class_docstr: Implementation of the Knockoff Nets attack from Orekondy et al. (2018).
  
  | Paper link: https://arxiv.org/abs/1812.02766
docstr: Select data to attack.
  
  :param x: An array with the source input to the victim classifier.
  :return: An array with the selected input to the victim classifier.
docstr_tok: ["Select","data","to","attack",".",":","param","x",":","An","array","with","the","source","input","to","the","victim","classifier",".",":","return",":","An","array","with","the","selected","input","to","the","victim","classifier","."]
code: def _select_data(self, x: np.ndarray) -> np.ndarray:
      """
      Select data to attack.
  
      :param x: An array with the source input to the victim classifier.
      :return: An array with the selected input to the victim classifier.
      """
      nb_stolen = np.minimum(self.nb_stolen, x.shape[0])
      rnd_index = np.random.choice(x.shape[0], nb_stolen, replace=False)
  
      return x[rnd_index].astype(ART_NUMPY_DTYPE)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, TYPE_CHECKING, Union
  import numpy
  from tqdm.auto import trange
  from art.config import ART_NUMPY_DTYPE
  from art.attacks.attack import ExtractionAttack
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification.classifier import ClassifierMixin
  from art.utils import to_categorical
label_desc: Use image node_id 4 for calling the KnockoffNets obj's underlying member method code with example usage: obj._select_data(x) and returns: x