def gradient(self):
self._update_approx()<EOL>g = self._ep.lml_derivatives(self._X)<EOL>ed = exp(-self.logitdelta)<EOL>es = exp(self.logscale)<EOL>grad = dict()<EOL>grad["<STR_LIT>"] = g["<STR_LIT>"] * (ed / (<NUM_LIT:1> + ed)) / (<NUM_LIT:1> + ed)<EOL>grad["<STR_LIT>"] = g["<STR_LIT>"] * es<EOL>grad["<STR_LIT>"] = g["<STR_LIT>"]<EOL>return grad<EOL>
r"""Gradient of the log of the marginal likelihood.

        Returns
        -------
        dict
            Map between variables to their gradient values.