class_docstr: Mixin class for smoothed estimators.
docstr: Perform prediction for a batch of inputs.
  
  :param x: Input samples.
  :param batch_size: Size of batches.
  :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
  :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
docstr_tok: ["Perform","prediction","for","a","batch","of","inputs",".",":","param","x",":","Input","samples",".",":","param","batch_size",":","Size","of","batches",".",":","param","training_mode",":","`","True","`","for","model","set","to","training","mode","and","`","'False","`","for","model","set","to","evaluation","mode",".",":","return",":","Array","of","predictions","of","shape","`","(","nb_inputs",",","nb_classes",")","`","."]
code: def _predict_classifier(
      self,
      x: np.ndarray,
      batch_size: int,
      training_mode: bool,
      **kwargs,
  ) -> np.ndarray:
      """
      Perform prediction for a batch of inputs.
  
      :param x: Input samples.
      :param batch_size: Size of batches.
      :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
      :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
      """
      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 2 for calling the DeRandomizedSmoothingMixin obj's underlying member method code with example usage: obj._predict_classifier(x, batch_size, training_mode) without return types