def _outer_values_update(self, full_values):
super(BayesianGPLVMMiniBatch, self)._outer_values_update(full_values)<EOL>if self.has_uncertain_inputs():<EOL><INDENT>meangrad_tmp, vargrad_tmp = self.kern.gradients_qX_expectations(<EOL>variational_posterior=self.X,<EOL>Z=self.Z, dL_dpsi0=full_values['<STR_LIT>'],<EOL>dL_dpsi1=full_values['<STR_LIT>'],<EOL>dL_dpsi2=full_values['<STR_LIT>'],<EOL>psi0=self.psi0, psi1=self.psi1, psi2=self.psi2)<EOL>self.X.mean.gradient = meangrad_tmp<EOL>self.X.variance.gradient = vargrad_tmp<EOL><DEDENT>else:<EOL><INDENT>self.X.gradient = self.kern.gradients_X(full_values['<STR_LIT>'], self.X, self.Z)<EOL>self.X.gradient += self.kern.gradients_X_diag(full_values['<STR_LIT>'], self.X)<EOL><DEDENT>
Here you put the values, which were collected before in the right places.
E.g. set the gradients of parameters, etc.