Hi,

While solving a conic optimization problem, I needed to compute its dual, so I used Dualization.jl. However, it does not support the constraints modeled by SumOfSquares.jl, resulting in the following error:

`Constraints of the Function MathOptInterface.VectorAffineFunction{Float64} in the set SumOfSquares.SOSPolynomialSet{FullSpace, DynamicPolynomials.Monomial{DynamicPolynomials.Commutative{DynamicPolynomials.CreationOrder}, Graded{LexOrder}}, MonomialVector{DynamicPolynomials.Commutative{DynamicPolynomials.CreationOrder}, Graded{LexOrder}}, SumOfSquares.Certificate.Newton{CopositiveInner{SOSCone}, MonomialBasis, SumOfSquares.Certificate.NewtonFilter{SumOfSquares.Certificate.NewtonDegreeBounds{Tuple{}}}}} are not yet implemented.`

I am wondering how it can solve the dual problem nonetheless.

```
using JuMP, MosekTools, LinearAlgebra, SumOfSquares, DynamicPolynomials, Dualization
# Define the data for the optimization problems
Q = [0.0 0.0 0.0; 0.0 1.0 2.0; 0.0 2.0 3.0]
A = [0.0 0.5 0.5; 0.5 0.0 0.0; 0.5 0.0 0.0]
b = 2
n = size(Q, 1)
# Define the dual optimization problem
model_dual = Model(dual_optimizer(Mosek.Optimizer))
@variable(model_dual, v)
@variable(model_dual, Y[1:n, 1:n], Symmetric)
# Using CopositiveInner
@polyvar x[1:n]
@constraint(model_dual, sum(x[i] * Y[i, j] * x[j] for i in 1:n for j in 1:n) in CopositiveInner(SOSCone()))
@objective(model_dual, Max, v * b)
@constraint(model_dual, Q - v * A - Y .== 0)
# Optimize the dual model
optimize!(model_dual)
solution_summary(model_dual)
# dual_model = dualize(model_dual)
```