Keep getting error with provide_rule

[KapilKhanal]

I have a very similar function that gets me matrices and vectors from an external geometry module (here using Python and returns a sample output) for which I plan to finite difference when needed in the AD chain.

`using PyCall
py"""
def demopython(r,p):
return [r,r^3-4*r^2,r^2/2]
"""

demojulia(r,p) = [r,r^3-4*r^2,r^2/2]

function trysolve(r)
p = ()
meshdata = provide_rule(py"demopython",r,p;mode="ffd")
return meshdata
end

r = 1.0
J1 = ForwardDiff.derivative(trysolve,r)
print(J1)`

When run, this gives the stacktrace below

`ERROR: MethodError: no method matching Float64(::ForwardDiff.Dual{ForwardDiff.Tag{typeof(trysolve), Float64}, 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.Log4π)
@ IrrationalConstants ~/.julia/packages/IrrationalConstants/vp5v4/src/macro.jl:112
...

Stacktrace:
[1] convert(::Type{Float64}, x::ForwardDiff.Dual{ForwardDiff.Tag{typeof(trysolve), Float64}, Float64, 1})
@ Base ./number.jl:7
[2] cconvert(T::Type, x::ForwardDiff.Dual{ForwardDiff.Tag{typeof(trysolve), Float64}, Float64, 1})
@ Base ./essentials.jl:543
[3] macro expansion
@ ~/.julia/packages/PyCall/1gn3u/src/exception.jl:108 [inlined]
[4] PyObject(r::ForwardDiff.Dual{ForwardDiff.Tag{typeof(trysolve), Float64}, Float64, 1})
@ PyCall ~/.julia/packages/PyCall/1gn3u/src/conversions.jl:23
[5] _pycall!(ret::PyObject, o::PyObject, args::Tuple{ForwardDiff.Dual{…}, Tuple{}}, nargs::Int64, kw::Ptr{Nothing})
@ PyCall ~/.julia/packages/PyCall/1gn3u/src/pyfncall.jl:24
[6] _pycall!
@ ~/.julia/packages/PyCall/1gn3u/src/pyfncall.jl:11 [inlined]
[7] PyObject
@ ~/.julia/packages/PyCall/1gn3u/src/pyfncall.jl:86 [inlined]
[8] _provide_rule
@ ~/.julia/packages/ImplicitAD/bF0uI/src/external.jl:21 [inlined]
[9] #provide_rule#80
@ ~/.julia/packages/ImplicitAD/bF0uI/src/external.jl:19 [inlined]
[10] trysolve(r::ForwardDiff.Dual{ForwardDiff.Tag{typeof(trysolve), Float64}, Float64, 1})
@ Main ~/Desktop/BEM.jl/rankineAD.jl:66
[11] derivative(f::typeof(trysolve), x::Float64)
@ ForwardDiff ~/.julia/packages/ForwardDiff/PcZ48/src/derivative.jl:14
[12] top-level scope
@ ~/Desktop/BEM.jl/rankineAD.jl:72`

[KapilKhanal]

with julia function 'demojulia(r,p) = [r,r^3-4*r^2,r^2/2]' the provide_rule works fine but with external code it seems to not work.