Here you put the values, which were collected before in the right places.
        E.g. set the gradients of parameters, etc.

def _outer_values_update(self, full_values):
        """
        Here you put the values, which were collected before in the right places.
        E.g. set the gradients of parameters, etc.
        """
        super(BayesianGPLVMMiniBatch, self)._outer_values_update(full_values)
        if self.has_uncertain_inputs():
            meangrad_tmp, vargrad_tmp = self.kern.gradients_qX_expectations(
                                            variational_posterior=self.X,
                                            Z=self.Z, dL_dpsi0=full_values['dL_dpsi0'],
                                            dL_dpsi1=full_values['dL_dpsi1'],
                                            dL_dpsi2=full_values['dL_dpsi2'],
                                            psi0=self.psi0, psi1=self.psi1, psi2=self.psi2)

            self.X.mean.gradient = meangrad_tmp
            self.X.variance.gradient = vargrad_tmp
        else:
            self.X.gradient = self.kern.gradients_X(full_values['dL_dKnm'], self.X, self.Z)
            self.X.gradient += self.kern.gradients_X_diag(full_values['dL_dKdiag'], self.X)