def _is_adversarial(self, x_adv: np.ndarray, y_true: np.ndarray) -> bool:
        """
        Check if example is adversarial.

        :param x_adv: Current example.
        :param y_true: True label of `x`.
        :return: Boolean if `x` is mis-classified.
        """
        y_prediction = self.estimator.predict(x=x_adv)

        if self.targeted:
            return np.argmax(y_prediction, axis=1)[0] == np.argmax(y_true, axis=1)[0]

        return np.argmax(y_prediction, axis=1)[0] != np.argmax(y_true, axis=1)[0]

        Check if example is adversarial.

        :param x_adv: Current example.
        :param y_true: True label of `x`.
        :return: Boolean if `x` is mis-classified.
        
art/attacks/evasion/geometric_decision_based_attack.py