StochasticCost as a feature of layers

In the past, subsampling of PauliTerms from input PauliSums was taken care of by an option in the SGDifferentiator module. However, there were a few issues with its implementation there:

1. Instead of subsampling PauliTerms from PauliSums, it actually changed the shape of the measurement tensor by subsampling PauliSums.
2. Had a python implementation instead of a C++ op backend.
3. Does not fit the new differentiator interface being implemented in #409 .

Fortunately, in discussions on the design doc for the new differentiators, it looks like this could be a feature we still want to keep, and pull up to the level of tfq.layers. Since, this subsampling of PauliTerms can speed up the estimation of expectation values in any context, not just when seeking gradients. See also #230 for the possible interaction of such a feature with Engine. Thinking to take this on in the near future. Thoughts on this @MichaelBroughton @jaeyoo ?

Start by reading tensorflow_quantum/python/differentiators/stochastic_differentiator.py and the new differentiator interface described in #409. Review the design discussion and #230 for the intended layer-level behavior; the work is complete when PauliTerm subsampling fits the interface, uses a C++ op backend, and works across expectation-value contexts.