class_docstr: Class performing adversarial training following Adversarial Weight Perturbation (AWP) protocol.
  
  | Paper link: https://proceedings.neurips.cc/paper/2020/file/1ef91c212e30e14bf125e9374262401f-Paper.pdf
class_docstr_tok: ["Class","performing","adversarial","training","following","Adversarial","Weight","Perturbation","(","AWP",")","protocol",".","|","Paper","link",":","https",":","\/\/proceedings.neurips.cc\/paper\/2020\/file\/1ef91c212e30e14bf125e9374262401f-Paper.pdf"]
docstr: Create an :class:`.AdversarialTrainerAWPPyTorch` instance.
  
  :param classifier: Model to train adversarially.
  :param proxy_classifier: Model for adversarial weight perturbation.
  :param attack: attack to use for data augmentation in adversarial training.
  :param mode: mode determining the optimization objective of base adversarial training and weight perturbation
         step
  :param gamma: The scaling factor controlling norm of weight perturbation relative to model parameters' norm.
  :param beta: The scaling factor controlling tradeoff between clean loss and adversarial loss for TRADES protocol
  :param warmup: The number of epochs after which weight perturbation is applied
docstr_tok: ["Create","an",":","class",":","`",".AdversarialTrainerAWPPyTorch","`","instance",".",":","param","classifier",":","Model","to","train","adversarially",".",":","param","proxy_classifier",":","Model","for","adversarial","weight","perturbation",".",":","param","attack",":","attack","to","use","for","data","augmentation","in","adversarial","training",".",":","param","mode",":","mode","determining","the","optimization","objective","of","base","adversarial","training","and","weight","perturbation","step",":","param","gamma",":","The","scaling","factor","controlling","norm","of","weight","perturbation","relative","to","model","parameters","'","norm",".",":","param","beta",":","The","scaling","factor","controlling","tradeoff","between","clean","loss","and","adversarial","loss","for","TRADES","protocol",":","param","warmup",":","The","number","of","epochs","after","which","weight","perturbation","is","applied"]
code: def __init__(
      self,
      classifier: PyTorchClassifier,
      proxy_classifier: PyTorchClassifier,
      attack: EvasionAttack,
      mode: str,
      gamma: float,
      beta: float,
      warmup: int,
  ):
      """
      Create an :class:`.AdversarialTrainerAWPPyTorch` instance.
  
      :param classifier: Model to train adversarially.
      :param proxy_classifier: Model for adversarial weight perturbation.
      :param attack: attack to use for data augmentation in adversarial training.
      :param mode: mode determining the optimization objective of base adversarial training and weight perturbation
             step
      :param gamma: The scaling factor controlling norm of weight perturbation relative to model parameters' norm.
      :param beta: The scaling factor controlling tradeoff between clean loss and adversarial loss for TRADES protocol
      :param warmup: The number of epochs after which weight perturbation is applied
      """
      super().__init__(
          classifier,
          proxy_classifier,
          attack,
          mode,
          gamma,
          beta,
          warmup,
      )
      self._classifier: PyTorchClassifier
      self._proxy_classifier: PyTorchClassifier
      self._attack: EvasionAttack
      self._mode: str
      self.gamma: float
      self._beta: float
      self._warmup: int
      self._apply_wp: bool
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import time
  from typing import Optional, Tuple, TYPE_CHECKING, List, Dict
  from collections import OrderedDict
  import numpy
  from tqdm.auto import trange
  from art.defences.trainer.adversarial_trainer_awp import AdversarialTrainerAWP
  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 AdversarialTrainerAWPPyTorch object from inherited base classes: AdversarialTrainerAWP with example: obj = AdversarialTrainerAWPPyTorch(classifier, proxy_classifier, attack, mode, gamma, beta, warmup)