class_docstr: Implementation of the Knockoff Nets attack from Orekondy et al. (2018).
  
  | Paper link: https://arxiv.org/abs/1812.02766
docstr: Query the victim classifier.
  
  :param x: An array with the source input to the victim classifier.
  :return: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)`.
docstr_tok: ["Query","the","victim","classifier",".",":","param","x",":","An","array","with","the","source","input","to","the","victim","classifier",".",":","return",":","Target","values","(","class","labels",")","one-hot-encoded","of","shape","`","(","nb_samples",",","nb_classes",")","`","."]
code: def _query_label(self, x: np.ndarray) -> np.ndarray:
      """
      Query the victim classifier.
  
      :param x: An array with the source input to the victim classifier.
      :return: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)`.
      """
      labels = self.estimator.predict(
          x=x, batch_size=self.batch_size_query
      )
      if not self.use_probability:
          labels = np.argmax(labels, axis=1)
          labels = to_categorical(
              labels=labels, nb_classes=self.estimator.nb_classes
          )
  
      return labels
  
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 5 for calling the KnockoffNets obj's underlying member method code with example usage: obj._query_label(x) and returns: labels