class_docstr: This is abstract class for different backend-specific implementations of TRADES protocol
  for adversarial training.
  
  | Paper link: https://proceedings.mlr.press/v97/zhang19p.html
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.
      """
      return self._classifier.predict(x, **kwargs)
  
file_path: adversarial-robustness-toolbox/art/defences/trainer/adversarial_trainer_trades.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import abc
  from typing import Optional, Tuple, TYPE_CHECKING
  import numpy
  from art.defences.trainer.trainer import Trainer
  from art.attacks.attack import EvasionAttack
  from art.data_generators import DataGenerator
label_desc: Use image node_id 4 for calling the AdversarialTrainerTRADES obj's underlying member method code with example usage: obj.predict(x) and returns: self