Fix for new GPflow heteroskedastic likelihood breaks for quadrature dependent likelihoods

[SebastianPopescu]

A clear and concise description of what the bug is.
In #84 there were several changes made to accommodate the new framework in GPflow for heteroskedastic likelihoods. More precisely, no_X = None in gpflux/layers/likelihood_layer.py.

This works well with Gaussian or Student-t likelihoods, however it will break when using Softmax, which uses quadrature for variational_expectations or predict_mean_and_var. Both methods require access to the shape of X, so because currently we are passing None this results in an error.