def __init__(
        self,
        classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
        attacker: EvasionAttack,
        method: str = "iterative_saliency",
        frame_index: int = 1,
        batch_size: int = 1,
        verbose: bool = True,
    ):
        """
        :param classifier: A trained classifier.
        :param attacker: An adversarial evasion attacker which supports masking. Currently supported:
                         ProjectedGradientDescent, BasicIterativeMethod, FastGradientMethod.
        :param method: Specifies which method to use: "iterative_saliency" (adds perturbation iteratively to frame
                       with the highest saliency score until attack is successful), "iterative_saliency_refresh"
                       (updates perturbation after each iteration), "one_shot" (adds all perturbations at once, i.e.
                       defaults to original attack).
        :param frame_index: Index of the axis in input (feature) array `x` representing the frame dimension.
        :param batch_size: Size of the batch on which adversarial samples are generated.
        :param verbose: Show progress bars.
        """
        super().__init__(estimator=classifier)

        self.attacker = attacker
        self.method = method
        self.frame_index = frame_index
        self.batch_size = batch_size
        self.verbose = verbose
        self._check_params()

        :param classifier: A trained classifier.
        :param attacker: An adversarial evasion attacker which supports masking. Currently supported:
                         ProjectedGradientDescent, BasicIterativeMethod, FastGradientMethod.
        :param method: Specifies which method to use: "iterative_saliency" (adds perturbation iteratively to frame
                       with the highest saliency score until attack is successful), "iterative_saliency_refresh"
                       (updates perturbation after each iteration), "one_shot" (adds all perturbations at once, i.e.
                       defaults to original attack).
        :param frame_index: Index of the axis in input (feature) array `x` representing the frame dimension.
        :param batch_size: Size of the batch on which adversarial samples are generated.
        :param verbose: Show progress bars.
        
art/attacks/evasion/frame_saliency.py