Workaround v1.10 function redefinition issue

JuliaLang/julia#52635

Author: ChrisRackauckas

src/derivative_wrappers.jl

@@ -6,7 +6,7 @@ const FIRST_AUTODIFF_TGRAD_MESSAGE = """
                                1. Turn off automatic differentiation (e.g. Rosenbrock23() becomes
                                   Rosenbrock23(autodiff=false)). More details can be found at
                                   https://docs.sciml.ai/DiffEqDocs/stable/features/performance_overloads/
-                               2. Improving the compatibility of `f` with ForwardDiff.jl automatic 
+                               2. Improving the compatibility of `f` with ForwardDiff.jl automatic
                                   differentiation (using tools like PreallocationTools.jl). More details
                                   can be found at https://docs.sciml.ai/DiffEqDocs/stable/basics/faq/#Autodifferentiation-and-Dual-Numbers
                                3. Defining analytical Jacobians and time gradients. More details can be
@@ -48,7 +48,7 @@ const FIRST_AUTODIFF_JAC_MESSAGE = """
                                1. Turn off automatic differentiation (e.g. Rosenbrock23() becomes
                                   Rosenbrock23(autodiff=false)). More details can befound at
                                   https://docs.sciml.ai/DiffEqDocs/stable/features/performance_overloads/
-                               2. Improving the compatibility of `f` with ForwardDiff.jl automatic 
+                               2. Improving the compatibility of `f` with ForwardDiff.jl automatic
                                   differentiation (using tools like PreallocationTools.jl). More details
                                   can be found at https://docs.sciml.ai/DiffEqDocs/stable/basics/faq/#Autodifferentiation-and-Dual-Numbers
                                3. Defining analytical Jacobians. More details can be

test/interface/autodiff_error_tests.jl

@@ -34,12 +34,13 @@ tspan = (0.0, 1.0)
 prob = ODEProblem(lorenz!, u0, tspan)
 @test_throws OrdinaryDiffEq.FirstAutodiffJacError solve(prob, Rosenbrock23())
 
-function lorenz!(du, u, p, t)
+function lorenz2!(du, u, p, t)
     du[1] = 10.0(u[2] - u[1])
     a[1] = t
     du[2] = u[1] * (28.0 - u[3]) - u[2]
     du[3] = u[1] * u[2] - (8 / 3) * u[3]
 end
+prob = ODEProblem(lorenz2!, u0, tspan)
 @test_throws OrdinaryDiffEq.FirstAutodiffTgradError solve(prob, Rosenbrock23())
 
 ## Test that nothing is using duals when autodiff=false