sion. This can be useful when the <code>C1</code>
dimension indexes different distributions, while <code>C0</code> indexes replicas of a
single distribution, all sampled in parallel.</p></li>
<li><p>Step size has shape <code>[C0, C1, 1]</code> or <code>[C0, C1, S]</code>, then no averaging will
happen. This means that each chain will learn its own step size. This can be
useful when all chains are sampling from different distributions. Even when
all chains are for the same distribution, this can help during 