ReverseDiff doesn't like DynamicPPL.ReshapeTransform

[penelopeysm]

#555 introduced DynamicPPL.ReshapeTransform, which is very nice, but there's what seems to be a bug in ReverseDiff.jl which causes it to fail when ReshapeTransform is composed with a broadcasted function.

I reported the upstream bug at JuliaDiff/ReverseDiff.jl#265. In the context of DynamicPPL, this occurs when we have something like the following:

using DynamicPPL: invlink_transform, ReshapeTransform
using ReverseDiff

f(x) = invlink_transform(InverseGamma(2, 3))
g(x) = ReshapeTransform(())(x)
h = f ∘ g
ReverseDiff.gradient(h, [1.0])

I suspect we should be able to change the implementation of ReshapeTransform though to try to circumvent this. I don't actually know all the possible shapes of stuff ReshapeTransform handles and whether different input/output shapes would give different ReverseDiff errors. However, I dug into a couple of the failing tests in Turing.jl, and it seems that both of them stem from ReshapeTransform being given singleton arrays (e.g. [1.0] above). Furthermore, the error message observed in all the other failing tests is the same (although I didn't verify that they ultimately stem from singleton arrays). So I think we could special-case this behaviour to keep ReverseDiff on our side.