Adjoint for ForwardDiff.jacobian

[mcabbott]

This adds one-line adjoint definitions for ForwardDiff.gradient and ForwardDiff.jacobian, using forwarddiff. This is a simpler version of what was proposed in #769.

Perhaps ideally, calling Zygote over ForwardDiff would actually do reverse mode over forward mode. The proposal in #769 was in fact to turn that into forward over reverse, while this PR is forward over forward. Maybe there are cases where one is much better than the other?

But it doesn't work at all right now. Errors involved are, BTW, these:

Mutating arrays is not supported

Need an adjoint for constructor ForwardDiff.Partials{1, Float64}. Gradient is of type Vector{ForwardDiff.Dual{ForwardDiff.Tag{var"#66#67", Float64}, Float64, 1}}

Closes #305 (gradient of a function containing Zygote.forward_jacobian, replace with ForwardDiff.jacobian) and maybe #953 (gradient of a function containing jacobian, could perhaps use ForwardDiff.jacobian).

[CarloLucibello]

this seems a good temporary solution until we have reverse over forward or forward over reverse. Maybe leave a comment over the new adjoints explaining this is doing forward over forward and link related discussions? I think we should merge then

[mcabbott]

Done. I also added derivative and hessian, not sure why I omitted them at first.

Note BTW the new failure on master, I think this is caused by JuliaLang/julia#40745

[CarloLucibello]

Nightly failed addressed in FluxML/IRTools.jl#86