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: Fit the classifier using the training data `(x, y)`.
  
  :param x: Features in array of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
            nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
  :param y: Target values (class labels in classification) in array of shape (nb_samples, nb_classes) in
            one-hot encoding format.
  :param kwargs: Dictionary of framework-specific arguments.
docstr_tok: ["Fit","the","classifier","using","the","training","data","`","(","x",",","y",")","`",".",":","param","x",":","Features","in","array","of","shape","(","nb_samples",",","nb_features",")","or","(","nb_samples",",","nb_pixels_1",",","nb_pixels_2",",","nb_channels",")","or","(","nb_samples",",","nb_channels",",","nb_pixels_1",",","nb_pixels_2",")",".",":","param","y",":","Target","values","(","class","labels","in","classification",")","in","array","of","shape","(","nb_samples",",","nb_classes",")","in","one-hot","encoding","format",".",":","param","kwargs",":","Dictionary","of","framework-specific","arguments","."]
code: def fit(self, x: np.ndarray, y: np.ndarray, **kwargs) -> None:
      """
      Fit the classifier using the training data `(x, y)`.
  
      :param x: Features in array of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
                nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
      :param y: Target values (class labels in classification) in array of shape (nb_samples, nb_classes) in
                one-hot encoding format.
      :param kwargs: Dictionary of framework-specific arguments.
      """
      raise NotImplementedError
  
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 4 for calling the QueryEfficientGradientEstimationClassifier obj's underlying member method code with example usage: obj.fit(x, y) without return types