class_docstr: Implements the functionality for albating the image, and retaining only a column
class_docstr_tok: ["Implements","the","functionality","for","albating","the","image",",","and","retaining","only","a","column"]
docstr: Creates an ablator which will retain columns by default, or rows if operating in row_ablation_mode
  
  :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.
  :param row_ablation_mode: if True then the ablator will function by retaining rows rather than columns.
docstr_tok: ["Creates","an","ablator","which","will","retain","columns","by","default",",","or","rows","if","operating","in","row_ablation_mode",":","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",".",":","param","row_ablation_mode",":","if","True","then","the","ablator","will","function","by","retaining","rows","rather","than","columns","."]
code: def __init__(
      self,
      ablation_size: int,
      channels_first: bool,
      row_ablation_mode: bool = False,
  ):
      """
      Creates an ablator which will retain columns by default, or rows if operating in row_ablation_mode
  
      :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.
      :param row_ablation_mode: if True then the ablator will function by retaining rows rather than columns.
      """
      super().__init__()
      self.ablation_size = ablation_size
      self.channels_first = channels_first
      self.row_ablation_mode = row_ablation_mode
  
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 ColumnAblator object from inherited base classes: BaseAblator with example: obj = ColumnAblator(ablation_size, channels_first, row_ablation_mode)