class_docstr: Class performing adversarial training following TRADES protocol.
  
  | Paper link: https://proceedings.mlr.press/v97/zhang19p.html
class_docstr_tok: ["Class","performing","adversarial","training","following","TRADES","protocol",".","|","Paper","link",":","https",":","\/\/proceedings.mlr.press\/v97\/zhang19p.html"]
docstr: Create an :class:`.AdversarialTrainerTRADESPyTorch` 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",":","`",".AdversarialTrainerTRADESPyTorch","`","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: PyTorchClassifier,
      attack: EvasionAttack,
      beta: float,
  ):
      """
      Create an :class:`.AdversarialTrainerTRADESPyTorch` 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
      """
      super().__init__(classifier, attack, beta)
      self._classifier: PyTorchClassifier
      self._attack: EvasionAttack
      self._beta: float
  
file_path: adversarial-robustness-toolbox/art/defences/trainer/adversarial_trainer_trades_pytorch.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import time
  from typing import Optional, Tuple, TYPE_CHECKING
  import numpy
  from tqdm.auto import trange
  from art.defences.trainer.adversarial_trainer_trades import AdversarialTrainerTRADES
  from art.estimators.classification.pytorch import PyTorchClassifier
  from art.data_generators import DataGenerator
  from art.attacks.attack import EvasionAttack
  from art.utils import check_and_transform_label_format
label_desc: Use image node_id 1 to create a new AdversarialTrainerTRADESPyTorch object from inherited base classes: AdversarialTrainerTRADES with example: obj = AdversarialTrainerTRADESPyTorch(classifier, attack, beta)