Evaluates the link function link(f) then computes the derivative of log likelihood using it
        Uses the Faa di Bruno's formula for the chain rule

        .. math::
            \\frac{d\\log p(y|\\lambda(f))}{df} = \\frac{d\\log p(y|\\lambda(f))}{d\\lambda(f)}\\frac{d\\lambda(f)}{df}

        :param f: latent variables f
        :type f: Nx1 array
        :param y: data
        :type y: Nx1 array
        :param Y_metadata: Y_metadata which is not used in student t distribution - not used
        :returns: derivative of log likelihood evaluated for this point
        :rtype: 1xN array

def dlogpdf_df(self, f, y, Y_metadata=None):
        """
        Evaluates the link function link(f) then computes the derivative of log likelihood using it
        Uses the Faa di Bruno's formula for the chain rule

        .. math::
            \\frac{d\\log p(y|\\lambda(f))}{df} = \\frac{d\\log p(y|\\lambda(f))}{d\\lambda(f)}\\frac{d\\lambda(f)}{df}

        :param f: latent variables f
        :type f: Nx1 array
        :param y: data
        :type y: Nx1 array
        :param Y_metadata: Y_metadata which is not used in student t distribution - not used
        :returns: derivative of log likelihood evaluated for this point
        :rtype: 1xN array
        """
        if isinstance(self.gp_link, link_functions.Identity):
            return self.dlogpdf_dlink(f, y, Y_metadata=Y_metadata)
        else:
            inv_link_f = self.gp_link.transf(f)
            dlogpdf_dlink = self.dlogpdf_dlink(inv_link_f, y, Y_metadata=Y_metadata)
            dlink_df = self.gp_link.dtransf_df(f)
            return chain_1(dlogpdf_dlink, dlink_df)