I am trying to compute gradient of a function with respect to Flux parameters.

Example:

```
using Flux, ForwardDiff
function f(x::Vector)
return [x[2]; -x[1] + (1 - x[1]^2) * x[2]]
end
# Function approximation
n = 2;
fhat = Chain(Dense(2,n),Dense(n,n,sigmoid),Dense(n,1));
ps = Flux.params(fhat);
# Define vector field and divergence
F(x) = f(x) * uhat(x)[1];
divF(x) = Flux.tr(ForwardDiff.jacobian(F,x));
# Compute gradients w.r.t function parameters
x = [1,2];
grads1 = gradient(() -> sum(F(x)), ps); @show grads[ps[1]] # Works as expected.
grads2 = gradient(() -> divF(x), ps); @show grads[ps[1]] # Nothing.
```

grads2 returns nothing

How can I obtain derivative of divF(x) w.r.t ps?

Thanks.