Fix adjoint test to use @static if instead of return

The previous approach using `return` didn't work because @testitem isn't a
function scope. Using @static if prevents the package imports from being
attempted at all on Julia 1.12+.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <[email protected]>

Author: ChrisRackauckas

test/adjoint_tests.jl

@@ -1,34 +1,33 @@
 @testitem "Adjoint Tests" tags = [:nopre] begin
     # Skip adjoint tests on Julia 1.12+ due to Enzyme/SciMLSensitivity compatibility issues
-    # To re-enable: change condition to `true` or `VERSION < v"1.13"`
-    if VERSION >= v"1.12"
-        @info "Skipping adjoint tests on Julia $(VERSION) - Enzyme/SciMLSensitivity not compatible with 1.12+"
-        return
-    end
-
-    using ForwardDiff, ReverseDiff, SciMLSensitivity, Tracker, Zygote, Enzyme, Mooncake
+    # To re-enable: change condition to `false` or `VERSION >= v"1.13"`
+    @static if VERSION < v"1.12"
+        using ForwardDiff, ReverseDiff, SciMLSensitivity, Tracker, Zygote, Enzyme, Mooncake
 
-    ff(u, p) = u .^ 2 .- p
+        ff(u, p) = u .^ 2 .- p
 
-    function solve_nlprob(p)
-        prob = NonlinearProblem{false}(ff, [1.0, 2.0], p)
-        sol = solve(prob, NewtonRaphson())
-        res = sol isa AbstractArray ? sol : sol.u
-        return sum(abs2, res)
-    end
+        function solve_nlprob(p)
+            prob = NonlinearProblem{false}(ff, [1.0, 2.0], p)
+            sol = solve(prob, NewtonRaphson())
+            res = sol isa AbstractArray ? sol : sol.u
+            return sum(abs2, res)
+        end
 
-    p = [3.0, 2.0]
+        p = [3.0, 2.0]
 
-    ∂p_zygote = only(Zygote.gradient(solve_nlprob, p))
-    ∂p_forwarddiff = ForwardDiff.gradient(solve_nlprob, p)
-    ∂p_tracker = Tracker.data(only(Tracker.gradient(solve_nlprob, p)))
-    ∂p_reversediff = ReverseDiff.gradient(solve_nlprob, p)
-    ∂p_enzyme = Enzyme.gradient(Enzyme.set_runtime_activity(Enzyme.Reverse), solve_nlprob, p)[1]
+        ∂p_zygote = only(Zygote.gradient(solve_nlprob, p))
+        ∂p_forwarddiff = ForwardDiff.gradient(solve_nlprob, p)
+        ∂p_tracker = Tracker.data(only(Tracker.gradient(solve_nlprob, p)))
+        ∂p_reversediff = ReverseDiff.gradient(solve_nlprob, p)
+        ∂p_enzyme = Enzyme.gradient(Enzyme.set_runtime_activity(Enzyme.Reverse), solve_nlprob, p)[1]
 
-    cache = Mooncake.prepare_gradient_cache(solve_nlprob, p)
-    ∂p_mooncake = Mooncake.value_and_gradient!!(cache, solve_nlprob, p)[2][2]
+        cache = Mooncake.prepare_gradient_cache(solve_nlprob, p)
+        ∂p_mooncake = Mooncake.value_and_gradient!!(cache, solve_nlprob, p)[2][2]
 
-    @test ∂p_zygote ≈ ∂p_tracker ≈ ∂p_reversediff ≈ ∂p_enzyme
-    @test ∂p_zygote ≈ ∂p_forwarddiff ≈ ∂p_tracker ≈ ∂p_reversediff ≈ ∂p_enzyme
-    @test_broken ∂p_forwarddiff ≈ ∂p_mooncake
+        @test ∂p_zygote ≈ ∂p_tracker ≈ ∂p_reversediff ≈ ∂p_enzyme
+        @test ∂p_zygote ≈ ∂p_forwarddiff ≈ ∂p_tracker ≈ ∂p_reversediff ≈ ∂p_enzyme
+        @test_broken ∂p_forwarddiff ≈ ∂p_mooncake
+    else
+        @info "Skipping adjoint tests on Julia $(VERSION) - Enzyme/SciMLSensitivity not compatible with 1.12+"
+    end
 end