mean-variance normalization layer

Normalizes activations to zero-mean unit-variance for each channel of each datum.

Unit tests included.
Hmm this was originally meant for vision tasks, so the MVN was done for each channel. I'm not sure how useful it might be for other tasks but if you feel it's useful, I can try to implement two behaviors based on a switch and move it to `common_layers`. Also for clarification, for general layers, should I just treat everything after `num` as one dimension?