Zygote return Any[] for expectation()

[agerlach]

using OrdinaryDiffEq, Distributions, DiffEqUncertainty,
        ForwardDiff, Zygote, FiniteDiff, DiffEqSensitivity

function simplependulum(du,u,p,t)
    g, L = p
    du[1] = u[2]
    du[2] = -(g/L)*sin(u[1])
end

u0 = [π/2, 0.0]
p = [9.807, 1.0]
tspan = (0.0,5.0)
prob = ODEProblem(simplependulum, u0, tspan, p)

u0_dist = [Uniform(π/2.1, π/1.9), 0.0]
p_dist = [9.807, truncated(Normal(1.0,.1), 0.6, 1.4)]

g(sol) = sol[1,end]

# ∂𝔼/∂u0
test_u0(q) = expectation(g, prob, q, p_dist, Koopman(), Tsit5())[1]
@time df1 = ForwardDiff.gradient(test_u0,u0) #Float64[]
@time df2 = FiniteDiff.finite_difference_gradient(test_u0,u0) #Float64[]
@time df3 = Zygote.gradient(test_u0,u0)    #any[]

# ∂𝔼/∂p
test_p(q) = expectation(g, prob, u0_dist, q, Koopman(), Tsit5())[1]
@time df1 = ForwardDiff.gradient(test_p,p) #Float64[]
@time df2 = FiniteDiff.finite_difference_gradient(test_p,p) #Float64[]
@time df3 = Zygote.gradient(test_p,p) @any[]

However, the following properly returns Float64[] for some reason

# mixed u0/p
function test_mixed(q) 
    _u = [u0_dist[1], q[1]]
    _p = [q[2], p_dist[1]]
    expectation(g, prob, _u, _p, Koopman(), Tsit5())[1]
end

x0 = [u0[2], p[1]]
@time df1 = ForwardDiff.gradient(test_mixed,x0) #Float64[]
@time df2 = FiniteDiff.finite_difference_gradient(test_mixed,x0) #Float64[]
@time df3 = Zygote.gradient(test_mixed,x0) #Float64[]