class_docstr: Implementation of the Geometric Decision-based Attack (GeoDA), a black-box attack requiring class predictions.
  Based on reference implementation: https://github.com/thisisalirah/GeoDA
  
  | Paper link: https://arxiv.org/abs/2003.06468
class_docstr_tok: ["Implementation","of","the","Geometric","Decision-based","Attack","(","GeoDA",")",",","a","black-box","attack","requiring","class","predictions",".","Based","on","reference","implementation",":","https",":","\/\/github.com\/thisisalirah\/GeoDA","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/2003.06468"]
docstr: Create a Geometric Decision-based Attack instance.
  
  :param estimator: A trained classifier.
  :param batch_size: The size of the batch used by the estimator during inference.
  :param norm: The norm of the adversarial perturbation. Possible values: "inf", np.inf, 1 or 2.
  :param sub_dim: Dimensionality of 2D frequency space (DCT).
  :param max_iter: Maximum number of iterations.
  :param bin_search_tol: Maximum remaining L2 perturbation defining binary search convergence. Input images are
                         normalised by maximal estimator.clip_value[1] if available or maximal value in the input
                         image.
  :param lambda_param: The lambda of equation 19 with `lambda_param=0` corresponding to a single iteration and
                       `lambda_param=1` to a uniform distribution of iterations per step.
  :param sigma: Variance of the Gaussian perturbation.
  :param targeted: Should the attack target one specific class.
  :param verbose: Show progress bars.
docstr_tok: ["Create","a","Geometric","Decision-based","Attack","instance",".",":","param","estimator",":","A","trained","classifier",".",":","param","batch_size",":","The","size","of","the","batch","used","by","the","estimator","during","inference",".",":","param","norm",":","The","norm","of","the","adversarial","perturbation",".","Possible","values",":","``","inf","''",",","np.inf",",","1","or","2.",":","param","sub_dim",":","Dimensionality","of","2D","frequency","space","(","DCT",")",".",":","param","max_iter",":","Maximum","number","of","iterations",".",":","param","bin_search_tol",":","Maximum","remaining","L2","perturbation","defining","binary","search","convergence",".","Input","images","are","normalised","by","maximal","estimator.clip_value","[","1","]","if","available","or","maximal","value","in","the","input","image",".",":","param","lambda_param",":","The","lambda","of","equation","19","with","`","lambda_param=0","`","corresponding","to","a","single","iteration","and","`","lambda_param=1","`","to","a","uniform","distribution","of","iterations","per","step",".",":","param","sigma",":","Variance","of","the","Gaussian","perturbation",".",":","param","targeted",":","Should","the","attack","target","one","specific","class",".",":","param","verbose",":","Show","progress","bars","."]
code: def __init__(
      self,
      estimator: "CLASSIFIER_TYPE",
      batch_size: int = 64,
      norm: Union[int, float, str] = 2,
      sub_dim: int = 10,
      max_iter: int = 4000,
      bin_search_tol: float = 0.1,
      lambda_param: float = 0.6,
      sigma: float = 0.0002,
      verbose: bool = True,
  ) -> None:
      """
      Create a Geometric Decision-based Attack instance.
  
      :param estimator: A trained classifier.
      :param batch_size: The size of the batch used by the estimator during inference.
      :param norm: The norm of the adversarial perturbation. Possible values: "inf", np.inf, 1 or 2.
      :param sub_dim: Dimensionality of 2D frequency space (DCT).
      :param max_iter: Maximum number of iterations.
      :param bin_search_tol: Maximum remaining L2 perturbation defining binary search convergence. Input images are
                             normalised by maximal estimator.clip_value[1] if available or maximal value in the input
                             image.
      :param lambda_param: The lambda of equation 19 with `lambda_param=0` corresponding to a single iteration and
                           `lambda_param=1` to a uniform distribution of iterations per step.
      :param sigma: Variance of the Gaussian perturbation.
      :param targeted: Should the attack target one specific class.
      :param verbose: Show progress bars.
      """
      super().__init__(estimator=estimator)
  
      self.batch_size = batch_size
      self.norm = norm
      self.sub_dim = sub_dim
      self.max_iter = max_iter
      self.bin_search_tol = bin_search_tol
      self.lambda_param = lambda_param
      self.sigma = sigma
      self._targeted = False
  
      self.verbose = verbose
      self._check_params()
  
      self.sub_basis: np.ndarray
      self.nb_calls = 0
      self.clip_min = 0.0
      self.clip_max = 0.0
      if self.estimator.input_shape is None:  # pragma: no cover
          raise ValueError(
              "The `input_shape` of the is required but None."
          )
      self.nb_channels = (
          self.estimator.input_shape[0]
          if self.estimator.channels_first
          else self.estimator.input_shape[2]
      )
  
      # Optimal number of iterations
      iteration = round(self.max_iter / 500)
      q_opt_it = int(self.max_iter - iteration * 25)
      _, iterate = self._opt_query_iteration(
          q_opt_it, iteration, self.lambda_param
      )
      q_opt_it = int(self.max_iter - iterate * 25)
      self.q_opt_iter, self.iterate = self._opt_query_iteration(
          q_opt_it, iteration, self.lambda_param
      )
  
imports: import os
  import math
  import logging
  from typing import List, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from tqdm.auto import trange
  from art.attacks.attack import EvasionAttack
  from art.config import ART_NUMPY_DTYPE
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification.classifier import ClassifierMixin
  from art.utils import check_and_transform_label_format, get_labels_np_array
label_desc: Use image node_id 1 to create a new GeoDA object from inherited base classes: EvasionAttack with example: obj = GeoDA(estimator, batch_size, norm, sub_dim, max_iter, bin_search_tol, lambda_param, sigma, verbose)