```
julia> using LinearAlgebra
julia> using ForwardDiff
julia> function f(x)
a=[1 2;3 4]
a=a.*x
_,s,_=svd(a)
return sum(s)
end
f (generic function with 1 method)
julia> ForwardDiff.derivative(f,2)
ERROR: MethodError: no method matching svd!(::Matrix{ForwardDiff.Dual{ForwardDiff.Tag{typeof(f), Int64}, Float64, 1}}; full=false, alg=LinearAlgebra.DivideAndConquer())
Closest candidates are:
svd!(::LinearAlgebra.AbstractTriangular; kwargs...) at ~/julia-1.7.2/share/julia/stdlib/v1.7/LinearAlgebra/src/triangular.jl:2565
svd!(::StridedMatrix{T}; full, alg) where T<:Union{Float32, Float64, ComplexF32, ComplexF64} at ~/julia-1.7.2/share/julia/stdlib/v1.7/LinearAlgebra/src/svd.jl:97
svd!(::StridedVector{T}; full, alg) where T<:Union{Float32, Float64, ComplexF32, ComplexF64} at ~/julia-1.7.2/share/julia/stdlib/v1.7/LinearAlgebra/src/svd.jl:106
...
Stacktrace:
[1] svd(A::Matrix{ForwardDiff.Dual{ForwardDiff.Tag{typeof(f), Int64}, Int64, 1}}; full::Bool, alg::LinearAlgebra.DivideAndConquer)
@ LinearAlgebra ~/julia-1.7.2/share/julia/stdlib/v1.7/LinearAlgebra/src/svd.jl:176
[2] svd(A::Matrix{ForwardDiff.Dual{ForwardDiff.Tag{typeof(f), Int64}, Int64, 1}})
@ LinearAlgebra ~/julia-1.7.2/share/julia/stdlib/v1.7/LinearAlgebra/src/svd.jl:176
[3] f(x::ForwardDiff.Dual{ForwardDiff.Tag{typeof(f), Int64}, Int64, 1})
@ Main ./REPL[3]:4
[4] derivative(f::typeof(f), x::Int64)
@ ForwardDiff ~/.julia/packages/ForwardDiff/wAaVJ/src/derivative.jl:14
[5] top-level scope
@ REPL[4]:1
```

I want to use Forwarddiff.jl to take the derivative of my function, but it seems that it does not support svd. How can I solve this problem? I don’t want to use Zygote.gradient(), which consumes more time and space memory than forward differential