24edeb20221803c592d9bc2c4f428fb9697358b7,gpytorch/inference/posterior_models/variational_gp_posterior.py,_VariationalGPPosterior,forward,#_VariationalGPPosterior#,65

Before Change

                alpha = Variable(self.alpha)
            else:
                alpha = gpytorch.variational_posterior_alpha(induc_induc_covar, self.variational_mean)
                self.alpha.resize_as_(alpha.data).copy_(alpha.data)
            test_mean = gpytorch.variational_posterior_mean(test_induc_covar, alpha)
            test_covar = gpytorch.variational_posterior_covar(test_induc_covar, induc_test_covar,
                                                              self.chol_variational_covar, test_test_covar,

After Change

            test_test_covar = full_covar[n:, n:]

            // Calculate posterior components
            if not hasattr(self, "alpha"):
                self.alpha = gpytorch.variational_posterior_alpha(induc_induc_covar, self.variational_mean)
            test_mean = gpytorch.variational_posterior_mean(test_induc_covar, self.alpha)
            test_covar = gpytorch.variational_posterior_covar(test_induc_covar, induc_test_covar,
                                                              self.chol_variational_covar, test_test_covar,
                                                              induc_induc_covar)

Instances