class_docstr: Class performing certified adversarial training from methods such as
  
  | Paper link: http://proceedings.mlr.press/v80/mirman18b/mirman18b.pdf
  | Paper link: https://arxiv.org/pdf/1810.12715.pdf
class_docstr_tok: ["Class","performing","certified","adversarial","training","from","methods","such","as","|","Paper","link",":","http",":","\/\/proceedings.mlr.press\/v80\/mirman18b\/mirman18b.pdf","|","Paper","link",":","https",":","\/\/arxiv.org\/pdf\/1810.12715.pdf"]
docstr: Perform prediction using the adversarially trained classifier using zonotopes
  
  :param cent: The datapoint, representing the zonotope center.
  :param bound: The perturbation range for the zonotope.
docstr_tok: ["Perform","prediction","using","the","adversarially","trained","classifier","using","zonotopes",":","param","cent",":","The","datapoint",",","representing","the","zonotope","center",".",":","param","bound",":","The","perturbation","range","for","the","zonotope","."]
code: def predict_zonotopes(
      self, cent: np.ndarray, bound, **kwargs
  ) -> Tuple[List[np.ndarray], List[np.ndarray]]:
      """
      Perform prediction using the adversarially trained classifier using zonotopes
  
      :param cent: The datapoint, representing the zonotope center.
      :param bound: The perturbation range for the zonotope.
      """
  
      if self.classifier.model.forward_mode != "abstract":
          raise ValueError(
              "For zonotope predictions, the model must be running in abstract mode. If a concrete "
              "prediction is wanted then use predict instead"
          )
  
      return self.classifier.predict_zonotopes(cent, bound, **kwargs)
  
file_path: adversarial-robustness-toolbox/art/defences/trainer/certified_adversarial_trainer_pytorch.py
imports: import logging
  import math
  import random
  import sys
  import numpy
  from sklearn.utils import shuffle
  from tqdm import tqdm
  from art.defences.trainer.trainer import Trainer
  from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent import ProjectedGradientDescent
  from art.utils import check_and_transform_label_format
label_desc: Use image node_id 4 for calling the AdversarialTrainerCertifiedPytorch obj's underlying member method code with example usage: obj.predict_zonotopes(cent, bound) and returns: self