4a1ec9808c64efb92d645c288c6d79e11a497fc4,gpytorch/variational/unwhitened_variational_strategy.py,UnwhitenedVariationalStrategy,forward,#UnwhitenedVariationalStrategy#Any#Any#Any#Any#,62

Before Change

        // Cache the kernel matrix with the cached CG calls
        if self.training:
            self._memoize_cache["prior_distribution_memo"] = MultivariateNormal(induc_mean, induc_induc_covar)

        // Compute predictive mean
        inv_products = induc_induc_covar.inv_matmul(induc_data_covar, left_tensors.transpose(-1, -2))

After Change

        // Cache the kernel matrix with the cached CG calls
        if self.training:
            prior_dist = MultivariateNormal(induc_mean, induc_induc_covar)
            add_to_cache(self, "prior_distribution_memo", prior_dist)

        // Compute predictive mean
        inv_products = induc_induc_covar.inv_matmul(induc_data_covar, left_tensors.transpose(-1, -2))
        predictive_mean = torch.add(test_mean, inv_products[..., 0, :])

Instances