Derivative issue with DiffResults and nested arrays

The following code works:

using ForwardDiff

g(x) = [[x^2, sqrt(x)]]
println(ForwardDiff.derivative(g, 2))

But this code:

using DiffResults
using ForwardDiff

g(x) = [[x^2, sqrt(x)]]
result = DiffResults.DiffResult(similar(g(2)), similar(g(2)))
result = ForwardDiff.derivative!(result, g, 2)
println(result)

Leads to a runtime error:

ERROR: LoadError: MethodError: no method matching Float64(::ForwardDiff.Dual{ForwardDiff.Tag{typeof(g), Int64}, Float64, 1})

Closest candidates are:
  (::Type{T})(::Real, ::RoundingMode) where T<:AbstractFloat
   @ Base rounding.jl:207
  (::Type{T})(::T) where T<:Number
   @ Core boot.jl:792
  Float64(::IrrationalConstants.Fourπ)
   @ IrrationalConstants C:\Users\bruno\.julia\packages\IrrationalConstants\vp5v4\src\macro.jl:112
  ...

Stacktrace:
  [1] convert(::Type{Float64}, x::ForwardDiff.Dual{ForwardDiff.Tag{typeof(g), Int64}, Float64, 1})
    @ Base .\number.jl:7
  [2] setindex!(A::Vector{Float64}, x::ForwardDiff.Dual{ForwardDiff.Tag{typeof(g), Int64}, Float64, 1}, i1::Int64)
    @ Base .\array.jl:1021
  [3] _unsafe_copyto!(dest::Vector{Float64}, doffs::Int64, src::Vector{ForwardDiff.Dual{…}}, soffs::Int64, n::Int64)
    @ Base .\array.jl:299
  [4] unsafe_copyto!
    @ .\array.jl:353 [inlined]
  [5] _copyto_impl!
    @ .\array.jl:376 [inlined]
  [6] copyto!
    @ .\array.jl:363 [inlined]
  [7] copyto!
    @ .\array.jl:385 [inlined]
  [8] copyto_axcheck!
    @ .\abstractarray.jl:1177 [inlined]
  [9] Vector{Float64}(x::Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(g), Int64}, Float64, 1}})
    @ Base .\array.jl:673
 [10] convert
    @ .\array.jl:665 [inlined]
 [11] setindex!
    @ .\array.jl:1021 [inlined]
 [12] map!
    @ .\abstractarray.jl:3279 [inlined]
 [13] value!
    @ C:\Users\bruno\.julia\packages\DiffResults\YLo25\src\DiffResults.jl:167 [inlined]
 [14] extract_value!
    @ C:\Users\bruno\.julia\packages\ForwardDiff\UBbGT\src\apiutils.jl:5 [inlined]
 [15] derivative!(result::DiffResults.MutableDiffResult{1, Vector{Vector{…}}, Tuple{Vector{…}}}, f::typeof(g), x::Int64)
    @ ForwardDiff C:\Users\bruno\.julia\packages\ForwardDiff\UBbGT\src\derivative.jl:49

Adding the following line fixes this problem:

ForwardDiff.value(::Type{T}, x::AbstractArray) where T = map(d -> ForwardDiff.value(T, d), x)

Reproduce the two examples in the issue and compare the stack-trace entries in DiffResults.jl:167, apiutils.jl:5, and derivative.jl:49. Inspect how nested arrays are handled by value! and extract_value!, then confirm that derivative! completes for g(x) without the reported conversion error.