code: def gradient(self):
          r
          self._update_approx()
  
          g = self._ep.lml_derivatives(self._X)
          ed = exp(-self.logitdelta)
          es = exp(self.logscale)
  
          grad = dict()
          grad["logitdelta"] = g["delta"] * (ed / (1 + ed)) / (1 + ed)
          grad["logscale"] = g["scale"] * es
          grad["beta"] = g["mean"]
  
          return grad
docs: r"""Gradient of the log of the marginal likelihood.
  
          Returns
          -------
          dict
              Map between variables to their gradient values.