def METHOD_NAME(self, X, raw_values=False):
    """Compute the decision function of the given observations.
    Parameters
    ----------
    X : array-like, shape (n_samples, n_features)
    raw_values : bool
        Whether or not to consider raw Mahalanobis distances as the
        decision function. Must be False (default) for compatibility
        with the others outlier detection tools.
    Returns
    -------
    decision : array-like, shape (n_samples, )
        The values of the decision function for each observations.
        It is equal to the Mahalanobis distances if `raw_values`
        is True. By default (``raw_values=True``), it is equal
        to the cubic root of the shifted Mahalanobis distances.
        In that case, the threshold for being an outlier is 0, which
        ensures a compatibility with other outlier detection tools
        such as the One-Class SVM.
    """
    check_is_fitted(self, 'threshold_')
    mahal_dist = self.mahalanobis(X)
    if raw_values:
        decision = mahal_dist
    else:
        check_is_fitted(self, 'threshold_')
        transformed_mahal_dist = mahal_dist ** 0.33
        decision = self.threshold_ ** 0.33 - transformed_mahal_dist
    return decision