 training data
and input test data <CODE>x_test</CODE>.
It returns the normal forward propagation through the network <CODE>y</CODE>
together with a matrix <CODE>extra</CODE> which consists of error bars (variance)
for a regression problem or moderated outputs for a classification problem.

<p>The optional argument (and return value) 
<CODE>invhess</CODE> is the inverse of the network Hessian
computed on the training data inputs and targets.  Passing it in avoids
recomputing it, which can be a sign