def __is_adversarial(self, image, predictions, in_bounds):
is_adversarial = self.__criterion.is_adversarial(<EOL>predictions, self.__original_class)<EOL>assert isinstance(is_adversarial, bool) orisinstance(is_adversarial, np.bool_)<EOL>if is_adversarial:<EOL><INDENT>is_best, distance = self.__new_adversarial(<EOL>image, predictions, in_bounds)<EOL><DEDENT>else:<EOL><INDENT>is_best = False<EOL>distance = None<EOL><DEDENT>return is_adversarial, is_best, distance<EOL>
Interface to criterion.is_adverarial that calls
        __new_adversarial if necessary.

        Parameters
        ----------
        predictions : :class:`numpy.ndarray`
            A vector with the pre-softmax predictions for some image.
        label : int
            The label of the unperturbed reference image.