def METHOD_NAME(  # type: ignore
    self,
    classifier: "CLASSIFIER_LOSS_GRADIENTS_TYPE",
    x: np.ndarray,
    y: np.ndarray,
    **kwargs: Union[str, bool, int, float],
) -> Tuple[List[float], List[float], float]:
    """
    Evaluate the Security Curve of a classifier using Projected Gradient Descent.
    :param classifier: A trained classifier that provides loss gradients.
    :param x: Input data to classifier for evaluation.
    :param y: True labels for input data `x`.
    :param kwargs: Keyword arguments for the Projected Gradient Descent attack used for evaluation, except keywords
                   `classifier` and `eps`.
    :return: List of evaluated `eps` values, List of adversarial accuracies, and benign accuracy.
    """
    kwargs.pop("classifier", None)
    kwargs.pop("eps", None)
    self.eps_list.clear()
    self.accuracy_adv_list.clear()
    self.accuracy = None
    # Check type of eps
    if isinstance(self.eps, int):
        if classifier.clip_values is not None:
            eps_increment = (classifier.clip_values[1] - classifier.clip_values[0]) / self.eps
        else:
            eps_increment = (np.max(x) - np.min(x)) / self.eps
        for i in range(1, self.eps + 1):
            self.eps_list.append(float(i * eps_increment))
    else:
        self.eps_list = [float(eps) for eps in self.eps]
    # Determine benign accuracy
    y_pred = classifier.predict(x=x, y=y)
    self.accuracy = self._get_accuracy(y=y, y_pred=y_pred)
    # Determine adversarial accuracy for each eps
    for eps in self.eps_list:
        attack_pgd = ProjectedGradientDescent(estimator=classifier, eps=eps, **kwargs)  # type: ignore
        x_adv = attack_pgd.generate(x=x, y=y)
        y_pred_adv = classifier.predict(x=x_adv, y=y)
        accuracy_adv = self._get_accuracy(y=y, y_pred=y_pred_adv)
        self.accuracy_adv_list.append(accuracy_adv)
    # Check gradients for potential obfuscation
    self._check_gradient(classifier=classifier, x=x, y=y, **kwargs)
    return self.eps_list, self.accuracy_adv_list, self.accuracy