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: Create an :class:`.AdversarialTrainerTRADES` instance.
  
  :param classifier: Model to train adversarially.
  :param attack: attack to use for data augmentation in adversarial training
  :param beta: The scaling factor controlling tradeoff between clean loss and adversarial loss
docstr_tok: ["Create","an",":","class",":","`",".AdversarialTrainerTRADES","`","instance",".",":","param","classifier",":","Model","to","train","adversarially",".",":","param","attack",":","attack","to","use","for","data","augmentation","in","adversarial","training",":","param","beta",":","The","scaling","factor","controlling","tradeoff","between","clean","loss","and","adversarial","loss"]
code: def __init__(
      self,
      classifier: "CLASSIFIER_LOSS_GRADIENTS_TYPE",
      attack: EvasionAttack,
      beta: float = 6.0,
  ):
      """
      Create an :class:`.AdversarialTrainerTRADES` instance.
  
      :param classifier: Model to train adversarially.
      :param attack: attack to use for data augmentation in adversarial training
      :param beta: The scaling factor controlling tradeoff between clean loss and adversarial loss
      """
      self._attack = attack
      self._beta = beta
      super().__init__(classifier)
  
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 1 to create a new AdversarialTrainerTRADES object from inherited base classes: Trainer, abc with example: obj = AdversarialTrainerTRADES(classifier, attack, beta)