class_docstr: Implements the functionality for albating the image, and retaining only a block
class_docstr_tok: ["Implements","the","functionality","for","albating","the","image",",","and","retaining","only","a","block"]
docstr: Creates an ablator which will retain blocks of the input data.
  
  :param ablation_size: Size of the column (or row if running in row_ablation_mode) to retain.
  :param channels_first: If the input data will be in channels first or channels last format.
docstr_tok: ["Creates","an","ablator","which","will","retain","blocks","of","the","input","data",".",":","param","ablation_size",":","Size","of","the","column","(","or","row","if","running","in","row_ablation_mode",")","to","retain",".",":","param","channels_first",":","If","the","input","data","will","be","in","channels","first","or","channels","last","format","."]
code: def __init__(self, ablation_size: int, channels_first: bool):
      """
      Creates an ablator which will retain blocks of the input data.
  
      :param ablation_size: Size of the column (or row if running in row_ablation_mode) to retain.
      :param channels_first: If the input data will be in channels first or channels last format.
      """
      super().__init__()
      self.ablation_size = ablation_size
      self.channels_first = channels_first
  
file_path: adversarial-robustness-toolbox/art/estimators/certification/derandomized_smoothing/ablators/tensorflow.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from typing import Optional, Union, Tuple, TYPE_CHECKING
  import random
  import numpy
  from art.estimators.certification.derandomized_smoothing.ablators.ablate import BaseAblator
label_desc: Use image node_id 1 to create a new BlockAblator object from inherited base classes: BaseAblator with example: obj = BlockAblator(ablation_size, channels_first)