def regularizer_diagonal(self, X, regularizer=None):
        r"""
        Get the diagonal of the prior variance on the weights (regularizer).

        This also returns a slice into the regression weights to which this
        regularizer applies.

        Parameters
        ----------
        X : ndarray
            (N, d) array of observations where N is the number of samples, and
            d is the dimensionality of X.
        regularizer : float, optional
            use this value instead of the value passed into the constructor for
            the regularizer.

        Returns
        -------
        ndarray :
            an array of shape (D,) that is the diagonal of the prior variance
            of the weights, :math:`\boldsymbol\Lambda` in :math:`\mathbf{w}
            \sim \mathcal{N}(\mathbf{0}, \boldsymbol\Lambda)`.
        slice:
            the slice into the regression weights that this regularizer
            applies to.

        Note
        ----
        You should not have to modify this method if you inherit from ``Basis``
        unless you are doing something very interesting...
        """
        reg = self.regularizer.value if regularizer is None else regularizer
        diag = np.full(self.get_dim(X), reg, dtype=float)
        return diag, slice(None)
r"""
        Get the diagonal of the prior variance on the weights (regularizer).

        This also returns a slice into the regression weights to which this
        regularizer applies.

        Parameters
        ----------
        X : ndarray
            (N, d) array of observations where N is the number of samples, and
            d is the dimensionality of X.
        regularizer : float, optional
            use this value instead of the value passed into the constructor for
            the regularizer.

        Returns
        -------
        ndarray :
            an array of shape (D,) that is the diagonal of the prior variance
            of the weights, :math:`\boldsymbol\Lambda` in :math:`\mathbf{w}
            \sim \mathcal{N}(\mathbf{0}, \boldsymbol\Lambda)`.
        slice:
            the slice into the regression weights that this regularizer
            applies to.

        Note
        ----
        You should not have to modify this method if you inherit from ``Basis``
        unless you are doing something very interesting...