class_docstr: Implementation of Randomized Smoothing applied to classifier predictions and gradients, as introduced
  in Cohen et al. (2019).
  
  | Paper link: https://arxiv.org/abs/1902.02918
class_docstr_tok: ["Implementation","of","Randomized","Smoothing","applied","to","classifier","predictions","and","gradients",",","as","introduced","in","Cohen","et","al",".","(","2019",")",".","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/1902.02918"]
docstr: Create a randomized smoothing wrapper.
  
  :param sample_size: Number of samples for smoothing.
  :param scale: Standard deviation of Gaussian noise added.
  :param alpha: The failure probability of smoothing.
docstr_tok: ["Create","a","randomized","smoothing","wrapper",".",":","param","sample_size",":","Number","of","samples","for","smoothing",".",":","param","scale",":","Standard","deviation","of","Gaussian","noise","added",".",":","param","alpha",":","The","failure","probability","of","smoothing","."]
code: def __init__(
      self,
      sample_size: int,
      *args,
      scale: float = 0.1,
      alpha: float = 0.001,
      **kwargs,
  ) -> None:
      """
      Create a randomized smoothing wrapper.
  
      :param sample_size: Number of samples for smoothing.
      :param scale: Standard deviation of Gaussian noise added.
      :param alpha: The failure probability of smoothing.
      """
      super().__init__(*args, **kwargs)  # type: ignore
      self.sample_size = sample_size
      self.scale = scale
      self.alpha = alpha
  
file_path: adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/randomized_smoothing.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from abc import ABC
  import logging
  from typing import Optional, Tuple
  import numpy
  from scipy.stats import norm
  from tqdm.auto import tqdm
  from art.config import ART_NUMPY_DTYPE
label_desc: Use image node_id 1 to create a new RandomizedSmoothingMixin object from inherited base classes: ABC with example: obj = RandomizedSmoothingMixin(sample_size)