class_docstr: Implementation of Query-Efficient Black-box Adversarial Examples. The attack approximates the gradient by
  maximizing the loss function over samples drawn from random Gaussian noise around the input.
  
  | Paper link: https://arxiv.org/abs/1712.07113
class_docstr_tok: ["Implementation","of","Query-Efficient","Black-box","Adversarial","Examples",".","The","attack","approximates","the","gradient","by","maximizing","the","loss","function","over","samples","drawn","from","random","Gaussian","noise","around","the","input",".","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/1712.07113"]
docstr: Generate samples around the current image.
  
  :param x: Sample input with shape as expected by the model.
  :param epsilon_map: Samples drawn from search space.
  :return: Two arrays of new input samples to approximate gradient.
docstr_tok: ["Generate","samples","around","the","current","image",".",":","param","x",":","Sample","input","with","shape","as","expected","by","the","model",".",":","param","epsilon_map",":","Samples","drawn","from","search","space",".",":","return",":","Two","arrays","of","new","input","samples","to","approximate","gradient","."]
code: def _generate_samples(
      self, x: np.ndarray, epsilon_map: np.ndarray
  ) -> Tuple[np.ndarray, np.ndarray]:
      """
      Generate samples around the current image.
  
      :param x: Sample input with shape as expected by the model.
      :param epsilon_map: Samples drawn from search space.
      :return: Two arrays of new input samples to approximate gradient.
      """
      minus = clip_and_round(
          np.repeat(x, self.num_basis, axis=0) - epsilon_map,
          self.clip_values,
          self.round_samples,
      )
      plus = clip_and_round(
          np.repeat(x, self.num_basis, axis=0) + epsilon_map,
          self.clip_values,
          self.round_samples,
      )
      return minus, plus
  
file_path: adversarial-robustness-toolbox/art/estimators/classification/query_efficient_bb.py
imports: import logging
  from typing import List, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from scipy.stats import entropy
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification.classifier import ClassifierMixin, ClassifierLossGradients
  from art.utils import clip_and_round
label_desc: Use image node_id 5 for calling the QueryEfficientGradientEstimationClassifier obj's underlying member method code with example usage: obj._generate_samples(x, epsilon_map) and returns: minus,  plus