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: Helper function to set the forward mode of the model
  
  :param mode: either concrete or abstract signifying how to run the forward pass
docstr_tok: ["Helper","function","to","set","the","forward","mode","of","the","model",":","param","mode",":","either","concrete","or","abstract","signifying","how","to","run","the","forward","pass"]
code: def set_forward_mode(self, mode: str) -> None:
      """
      Helper function to set the forward mode of the model
  
      :param mode: either concrete or abstract signifying how to run the forward pass
      """
      self.classifier.model.set_forward_mode(mode)
  
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 5 for calling the AdversarialTrainerCertifiedPytorch obj's underlying member method code with example usage: obj.set_forward_mode(mode) without return types