both the <code>kernel</code> and <code>bias</code>
distributions.</p>
<p>Upon being built, this layer adds losses (accessible via the <code>losses</code>
property) representing the divergences of <code>kernel</code> and/or <code>bias</code> surrogate
posteriors and their respective priors. When doing minibatch stochastic
optimization, make sure to scale this loss such that it is applied just once
per epoch (e.g. if <code>kl</code> is the sum of <code>losses</code> for each element of the batch,
you should p