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.
        """
        if self.has_uncertain_inputs():
            #gradients wrt kernel
            dL_dKmm = full_values['dL_dKmm']
            self.kern.update_gradients_full(dL_dKmm, self.Z, None)
            kgrad = self.kern.gradient.copy()
            self.kern.update_gradients_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'])
            self.kern.gradient += kgrad


            #gradients wrt Z
            self.Z.gradient = self.kern.gradients_X(dL_dKmm, self.Z)
            self.Z.gradient += self.kern.gradients_Z_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'])
        else:
            #gradients wrt kernel
            self.kern.update_gradients_diag(full_values['dL_dKdiag'], self.X)
            kgrad = self.kern.gradient.copy()
            self.kern.update_gradients_full(full_values['dL_dKnm'], self.X, self.Z)
            kgrad += self.kern.gradient
            self.kern.update_gradients_full(full_values['dL_dKmm'], self.Z, None)
            self.kern.gradient += kgrad
            #kgrad += self.kern.gradient

            #gradients wrt Z
            self.Z.gradient = self.kern.gradients_X(full_values['dL_dKmm'], self.Z)
            self.Z.gradient += self.kern.gradients_X(full_values['dL_dKnm'].T, self.Z, self.X)

        self.likelihood.update_gradients(full_values['dL_dthetaL'])