class_docstr: Mixin class for smoothed estimators.
docstr: Performs cumulative predictions over every ablation location
  
  :param x: Unablated image
  :param batch_size: the batch size for the prediction
  :param training_mode: if to run the classifier in training mode
  :return: cumulative predictions after sweeping over all the ablation configurations.
docstr_tok: ["Performs","cumulative","predictions","over","every","ablation","location",":","param","x",":","Unablated","image",":","param","batch_size",":","the","batch","size","for","the","prediction",":","param","training_mode",":","if","to","run","the","classifier","in","training","mode",":","return",":","cumulative","predictions","after","sweeping","over","all","the","ablation","configurations","."]
code: def predict(
      self,
      x: np.ndarray,
      batch_size: int = 128,
      training_mode: bool = False,
      **kwargs,
  ) -> np.ndarray:
      """
      Performs cumulative predictions over every ablation location
  
      :param x: Unablated image
      :param batch_size: the batch size for the prediction
      :param training_mode: if to run the classifier in training mode
      :return: cumulative predictions after sweeping over all the ablation configurations.
      """
      raise NotImplementedError
  
file_path: adversarial-robustness-toolbox/art/estimators/certification/derandomized_smoothing/derandomized.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from abc import ABC, abstractmethod
  import numpy
label_desc: Use image node_id 3 for calling the DeRandomizedSmoothingMixin obj's underlying member method code with example usage: obj.predict(x, batch_size, training_mode) without return types