157041a86ecda19d43967fc3028d7b48fe17b243,gpytorch/mlls/variational_elbo.py,VariationalELBO,forward,#VariationalELBO#Any#Any#,22

Before Change

        else:
            log_prior = torch.zeros_like(log_likelihood)
            for _, prior, closure, _ in self.named_priors():
                log_prior.add_(prior.log_prob(closure()).sum())
            if had_added_losses:
                return log_likelihood, kl_divergence, log_prior.div(self.num_data), added_loss
            else:
                return log_likelihood, kl_divergence, log_prior.div(self.num_data)

After Change

            log_prior.add_(prior.log_prob(closure()).sum().div(self.num_data))

        if self.combine_terms:
            return log_likelihood - kl_divergence + log_prior - added_loss
        else:
            if had_added_losses:
                return log_likelihood, kl_divergence, log_prior.div(self.num_data), added_loss
            else:

Instances