Add aux to Going Bayesian doc

docs/log_posteriors.md

@@ -98,9 +98,12 @@ either $N$ or $n$ increaase.
         log_prior = diag_normal_log_prob(params, sd=1., normalize=False)
         mean_log_lik = Categorical(logits=logits).log_prob(batch['labels']).mean()
         mean_log_post = log_prior / num_data + mean_log_lik
-        return mean_log_post
+        return mean_log_post, torch.tensor([])
     ```
 
+    See [auxiliary information](#auxiliary-information) for why we return an
+    additional empty tensor.
+
 The issue with running Bayesian methods (such as VI or SGHMC) on this mean log posterior
 function is that naive application will result in approximating the tempered posterior