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: Save a model to file specific to the backend framework.
  
  :param filename: Name of the file where to save the model.
  :param path: Path of the directory where to save the model. If no path is specified, the model will be stored in
               the default data location of ART at `ART_DATA_PATH`.
docstr_tok: ["Save","a","model","to","file","specific","to","the","backend","framework",".",":","param","filename",":","Name","of","the","file","where","to","save","the","model",".",":","param","path",":","Path","of","the","directory","where","to","save","the","model",".","If","no","path","is","specified",",","the","model","will","be","stored","in","the","default","data","location","of","ART","at","`","ART_DATA_PATH","`","."]
code: def save(self, filename: str, path: Optional[str] = None) -> None:
      """
      Save a model to file specific to the backend framework.
  
      :param filename: Name of the file where to save the model.
      :param path: Path of the directory where to save the model. If no path is specified, the model will be stored in
                   the default data location of ART at `ART_DATA_PATH`.
      """
      raise NotImplementedError
  
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 9 for calling the QueryEfficientGradientEstimationClassifier obj's underlying member method code with example usage: obj.save(filename, path) without return types