note about promotion (#94)

Author: mcabbott

src/destructure.jl

@@ -11,12 +11,20 @@ Differentiable.
 
 # Example
 ```jldoctest
-julia> v, re = destructure((x=[1.0, 2.0], y=(sin, [3 + 4im])))
+julia> v, re = destructure((x=[1.0, 2.0], y=(sin, [3.0 + 4.0im])))
 (ComplexF64[1.0 + 0.0im, 2.0 + 0.0im, 3.0 + 4.0im], Restructure(NamedTuple, ..., 3))
 
-julia> re([3, 5-im, 7+11im])
+julia> re([3, 5, 7+11im])
 (x = [3.0, 5.0], y = (sin, ComplexF64[7.0 + 11.0im]))
 ```
+
+If `model` contains various number types, they are promoted to make `vector`,
+and are usually restored by `Restructure`. Such restoration follows the rules 
+of `ChainRulesCore.ProjectTo`, and thus will restore floating point precision,
+but will permit more exotic numbers like `ForwardDiff.Dual`.
+
+If `model` contains only GPU arrays, then `vector` will also live on the GPU.
+At present, a mixture of GPU and ordinary CPU arrays is undefined behaviour.
 """
 function destructure(x)
   flat, off, len = _flatten(x)