class_docstr: Base class defining the methods used for the ablators.
docstr: Checks if based on the predictions supplied the classifications over the ablated datapoints result in a
  certified prediction against a patch attack of size size_to_certify.
  
  :param pred_counts: The cumulative predictions of the classifier over the ablation locations.
  :param size_to_certify: The size of the patch to check against.
  :param label: ground truth labels
code: def certify(
      self,
      pred_counts: np.ndarray,
      size_to_certify: int,
      label: Union[np.ndarray, "tf.Tensor"],
  ) -> Union[
      Tuple["tf.Tensor", "tf.Tensor", "tf.Tensor"],
      Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor"],
  ]:
      """
      Checks if based on the predictions supplied the classifications over the ablated datapoints result in a
      certified prediction against a patch attack of size size_to_certify.
  
      :param pred_counts: The cumulative predictions of the classifier over the ablation locations.
      :param size_to_certify: The size of the patch to check against.
      :param label: ground truth labels
      """
      raise NotImplementedError
  
file_path: adversarial-robustness-toolbox/art/estimators/certification/derandomized_smoothing/ablators/ablate.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from abc import ABC, abstractmethod
  from typing import Optional, Tuple, Union, TYPE_CHECKING
  import numpy
label_desc: Use image node_id 2 for calling the BaseAblator obj's underlying member method code with example usage: obj.certify(pred_counts, size_to_certify, label) without return types