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

        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
        
art/defences/trainer/adversarial_trainer_awp_pytorch.py