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.
  
  :param x: Input samples.
  :param kwargs: Other parameters to be passed on to the `predict` function of the classifier.
  :return: Predictions for test set.
docstr_tok: ["Perform","prediction","using","the","adversarially","trained","classifier",".",":","param","x",":","Input","samples",".",":","param","kwargs",":","Other","parameters","to","be","passed","on","to","the","`","predict","`","function","of","the","classifier",".",":","return",":","Predictions","for","test","set","."]
code: def predict(self, x: np.ndarray, **kwargs) -> np.ndarray:
      """
      Perform prediction using the adversarially trained classifier.
  
      :param x: Input samples.
      :param kwargs: Other parameters to be passed on to the `predict` function of the classifier.
      :return: Predictions for test set.
      """
      if self.classifier.model.forward_mode != "concrete":
          raise ValueError(
              "For normal predictions, the model must be running in concrete mode. If an abstract "
              "prediction is wanted then use predict_zonotopes instead"
          )
  
      return self.classifier.predict(x, **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 3 for calling the AdversarialTrainerCertifiedPytorch obj's underlying member method code with example usage: obj.predict(x) and returns: self