Lux does not handle sensitivity algorithms with ReverseDiff

SciML / SciMLSensitivity.jl

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

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using Zygote, Lux, OrdinaryDiffEq, DiffEqSensitivity, ComponentArrays, Random function system!(du, u, p, t, controller) α, β, γ, δ = 0.5f0, 1.0f0, 1.0f0, 1.0f0 y1, y2 = u c1, c2 = controller(u, p) y1_prime = -(c1 + α * c1^2) * y1 + δ * c2 y2_prime = (β * c1 - γ * c2) * y1 # return [y1_prime, y2_prime] # works @inbounds begin # fails du[1] = y1_prime du[2] = y2_prime end end function loss(params, prob, tsteps) sensealg=InterpolatingAdjoint(autojacvec=ReverseDiffVJP()) # fails # sensealg=InterpolatingAdjoint(autojacvec=ZygoteVJP()) # works sol = solve(prob, Tsit5(); p=params, saveat=tsteps, sensealg) # integrate ODE system return -Array(sol)[2, end] # second variable, last value, maximize end u0 = [1.0f0, 0.0f0] tspan = (0.0f0, 1.0f0) tsteps = 0.0f0:0.01f0:1.0f0 controller = Chain( Dense(2, 12, tanh), Dense(12, 12, tanh), Dense(12, 2), x -> 5 * Lux.σ.(x), # controllers ∈ (0, 5) ) init_params, init_states = Lux.setup(Random.default_rng(), controller) θ = ComponentArray(init_params) lux_controller(x, params) = controller(x, params, init_states)[1] dudt!(du, u, p, t) = system!(du, u, p, t, lux_controller) prob = ODEProblem(dudt!, u0, tspan, θ) loss(params) = loss(params, prob, tsteps) Zygote.gradient(loss, θ)

ERROR: MethodError: no method matching (::Chain{NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4), Tuple{Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, WrappedFunction{var"#7#8"}}}})(::ReverseDiff.TrackedArray{Float32, Float32, 1, Vector{Float32}, Vector{Float32}}, ::ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}}, ::NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4), NTuple{4, NamedTuple{(), Tuple{}}}}) Closest candidates are: (::Chain)(::Any, ::Union{ComponentArray, NamedTuple}, ::NamedTuple) at C:\Users\ilyao\.julia\packages\Lux\r2DyF\src\layers\basic.jl:519 Stacktrace: [1] lux_controller(x::ReverseDiff.TrackedArray{Float32, Float32, 1, Vector{Float32}, Vector{Float32}}, params::ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}}) @ Main .\REPL[51]:1 [2] system!(du::Vector{ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}}}}, u::ReverseDiff.TrackedArray{Float32, Float32, 1, Vector{Float32}, Vector{Float32}}, p::ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}}, t::ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 1, Vector{Float32}, Vector{Float32}}}, controller::typeof(lux_controller)) @ Main .\REPL[35]:6 [3] dudt! @ .\REPL[52]:1 [inlined] [4] ODEFunction @ C:\Users\ilyao\.julia\packages\SciMLBase\dYFnI\src\scimlfunctions.jl:1595 [inlined] [5] (::DiffEqSensitivity.var"#106#121"{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}})(u::ReverseDiff.TrackedArray{Float32, Float32, 1, Vector{Float32}, Vector{Float32}}, p::ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}}, t::ReverseDiff.TrackedArray{Float32, Float32, 1, Vector{Float32}, Vector{Float32}}) @ DiffEqSensitivity C:\Users\ilyao\.julia\packages\DiffEqSensitivity\GjhZ8\src\adjoint_common.jl:151 [6] ReverseDiff.GradientTape(f::Function, input::Tuple{Vector{Float32}, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, Vector{Float32}}, cfg::ReverseDiff.GradientConfig{Tuple{ReverseDiff.TrackedArray{Float32, Float32, 1, Vector{Float32}, Vector{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}}, ReverseDiff.TrackedArray{Float32, Float32, 1, Vector{Float32}, Vector{Float32}}}}) @ ReverseDiff C:\Users\ilyao\.julia\packages\ReverseDiff\5MMPp\src\api\tape.jl:207 [7] ReverseDiff.GradientTape(f::Function, input::Tuple{Vector{Float32}, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, Vector{Float32}}) @ ReverseDiff C:\Users\ilyao\.julia\packages\ReverseDiff\5MMPp\src\api\tape.jl:204 [8] adjointdiffcache(g::DiffEqSensitivity.var"#df#240"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, sensealg::InterpolatingAdjoint{0, true, Val{:central}, ReverseDiffVJP{false}, Bool}, discrete::Bool, sol::ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, dg::Nothing, f::ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}; quad::Bool, noiseterm::Bool, needs_jac::Bool) @ DiffEqSensitivity C:\Users\ilyao\.julia\packages\DiffEqSensitivity\GjhZ8\src\adjoint_common.jl:149 [9] DiffEqSensitivity.ODEInterpolatingAdjointSensitivityFunction(g::Function, sensealg::InterpolatingAdjoint{0, true, Val{:central}, ReverseDiffVJP{false}, Bool}, discrete::Bool, sol::ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, dg::Nothing, f::Function, checkpoints::Vector{Float32}, tols::NamedTuple{(:reltol, :abstol), Tuple{Float64, Float64}}, tstops::Nothing; noiseterm::Bool) @ DiffEqSensitivity C:\Users\ilyao\.julia\packages\DiffEqSensitivity\GjhZ8\src\interpolating_adjoint.jl:72 [10] DiffEqSensitivity.ODEInterpolatingAdjointSensitivityFunction(g::Function, sensealg::InterpolatingAdjoint{0, true, Val{:central}, ReverseDiffVJP{false}, Bool}, discrete::Bool, sol::ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, dg::Nothing, f::Function, checkpoints::Vector{Float32}, tols::NamedTuple{(:reltol, :abstol), Tuple{Float64, Float64}}, tstops::Nothing) @ DiffEqSensitivity C:\Users\ilyao\.julia\packages\DiffEqSensitivity\GjhZ8\src\interpolating_adjoint.jl:22 [11] ODEAdjointProblem(sol::ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, sensealg::InterpolatingAdjoint{0, true, Val{:central}, ReverseDiffVJP{false}, Bool}, g::DiffEqSensitivity.var"#df#240"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, t::Vector{Float32}, dg::Nothing; checkpoints::Vector{Float32}, callback::Nothing, reltol::Float64, abstol::Float64, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}) @ DiffEqSensitivity C:\Users\ilyao\.julia\packages\DiffEqSensitivity\GjhZ8\src\interpolating_adjoint.jl:260 [12] _adjoint_sensitivities(sol::ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, sensealg::InterpolatingAdjoint{0, true, Val{:central}, ReverseDiffVJP{false}, Bool}, alg::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, g::DiffEqSensitivity.var"#df#240"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, t::Vector{Float32}, dg::Nothing; abstol::Float64, reltol::Float64, checkpoints::Vector{Float32}, corfunc_analytical::Nothing, callback::Nothing, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}) @ DiffEqSensitivity C:\Users\ilyao\.julia\packages\DiffEqSensitivity\GjhZ8\src\sensitivity_interface.jl:286 [13] adjoint_sensitivities(::ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, ::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, ::Vararg{Any}; sensealg::InterpolatingAdjoint{0, true, Val{:central}, ReverseDiffVJP{false}, Bool}, kwargs::Base.Pairs{Symbol, Nothing, Tuple{Symbol}, NamedTuple{(:callback,), Tuple{Nothing}}}) @ DiffEqSensitivity C:\Users\ilyao\.julia\packages\DiffEqSensitivity\GjhZ8\src\sensitivity_interface.jl:271 [14] (::DiffEqSensitivity.var"#adjoint_sensitivity_backpass#239"{Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, InterpolatingAdjoint{0, true, Val{:central}, ReverseDiffVJP{false}, Bool}, Vector{Float32}, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, Tuple{}, Colon, NamedTuple{(), Tuple{}}})(Δ::Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) @ DiffEqSensitivity C:\Users\ilyao\.julia\packages\DiffEqSensitivity\GjhZ8\src\concrete_solve.jl:253 [15] ZBack @ C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\compiler\chainrules.jl:205 [inlined] [16] (::Zygote.var"#kw_zpullback#37"{DiffEqSensitivity.var"#adjoint_sensitivity_backpass#239"{Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, InterpolatingAdjoint{0, true, Val{:central}, ReverseDiffVJP{false}, Bool}, Vector{Float32}, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, Tuple{}, Colon, NamedTuple{(), Tuple{}}}})(dy::Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) @ Zygote C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\compiler\chainrules.jl:231 [17] SciML/DiffEqFlux.jl#208 @ C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\lib\lib.jl:207 [inlined] [18] (::Zygote.var"#1750#back#210"{Zygote.var"#208#209"{Tuple{NTuple{6, Nothing}, Tuple{Nothing}}, Zygote.var"#kw_zpullback#37"{DiffEqSensitivity.var"#adjoint_sensitivity_backpass#239"{Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, InterpolatingAdjoint{0, true, Val{:central}, ReverseDiffVJP{false}, Bool}, Vector{Float32}, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, Tuple{}, Colon, NamedTuple{(), Tuple{}}}}}})(Δ::Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) @ Zygote C:\Users\ilyao\.julia\packages\ZygoteRules\AIbCs\src\adjoint.jl:67 [19] Pullback @ C:\Users\ilyao\.julia\packages\DiffEqBase\R12pQ\src\solve.jl:710 [inlined] [20] (::typeof(∂(#solve#29)))(Δ::Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) @ Zygote C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0 [21] (::Zygote.var"#208#209"{Tuple{NTuple{6, Nothing}, Tuple{Nothing}}, typeof(∂(#solve#29))})(Δ::Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) @ Zygote C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\lib\lib.jl:207 [22] (::Zygote.var"#1750#back#210"{Zygote.var"#208#209"{Tuple{NTuple{6, Nothing}, Tuple{Nothing}}, typeof(∂(#solve#29))}})(Δ::Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) @ Zygote C:\Users\ilyao\.julia\packages\ZygoteRules\AIbCs\src\adjoint.jl:67 [23] Pullback @ C:\Users\ilyao\.julia\packages\DiffEqBase\R12pQ\src\solve.jl:703 [inlined] [24] (::typeof(∂(solve##kw)))(Δ::Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) @ Zygote C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0 [25] Pullback @ .\REPL[36]:4 [inlined] [26] (::typeof(∂(loss)))(Δ::Float32) @ Zygote C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0 [27] Pullback @ .\REPL[54]:1 [inlined] [28] (::typeof(∂(loss)))(Δ::Float32) @ Zygote C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0 [29] (::Zygote.var"#52#53"{typeof(∂(loss))})(Δ::Float32) @ Zygote C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\compiler\interface.jl:41 [30] gradient(f::Function, args::ComponentVector{Float32}) @ Zygote C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\compiler\interface.jl:76 [31] top-level scope @ REPL[56]:1

julia> Zygote.gradient(loss, θ) ERROR: type TrackedArray has no field layer_1 Stacktrace: [1] getproperty @ .\Base.jl:38 [inlined] [2] macro expansion @ C:\Users\ilyao\.julia\packages\Lux\x5I6q\src\layers\basic.jl:0 [inlined] [3] applychain(layers::NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4), Tuple{Dense{true, typeof(NNlib.tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Dense{true, typeof(NNlib.tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, WrappedFunction{var"#11#12"}}}, x::TrackedArray{…,Vector{Float32}}, ps::TrackedArray{…,ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}}, st::NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4), NTuple{4, NamedTuple{(), Tuple{}}}}) @ Lux C:\Users\ilyao\.julia\packages\Lux\x5I6q\src\layers\basic.jl:509 [4] (::Chain{NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4), Tuple{Dense{true, typeof(NNlib.tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Dense{true, typeof(NNlib.tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, WrappedFunction{var"#11#12"}}}})(x::TrackedArray{…,Vector{Float32}}, ps::TrackedArray{…,ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}}, st::NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4), NTuple{4, NamedTuple{(), Tuple{}}}}) @ Lux C:\Users\ilyao\.julia\packages\Lux\x5I6q\src\layers\basic.jl:506 [5] lux_controller(x::TrackedArray{…,Vector{Float32}}, params::TrackedArray{…,ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}}) @ Main .\REPL[37]:1 [6] system!(du::Vector{Tracker.TrackedReal{Float32}}, u::TrackedArray{…,Vector{Float32}}, p::TrackedArray{…,ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}}, t::Float32, controller::typeof(lux_controller)) @ Main .\REPL[15]:6 [7] dudt! @ .\REPL[24]:1 [inlined] [8] ODEFunction @ C:\Users\ilyao\.julia\packages\SciMLBase\dYFnI\src\scimlfunctions.jl:1595 [inlined] [9] #26 @ C:\Users\ilyao\.julia\packages\DiffEqSensitivity\SjURy\src\derivative_wrappers.jl:319 [inlined] [10] #20 @ C:\Users\ilyao\.julia\packages\Tracker\9xWLl\src\back.jl:148 [inlined] [11] forward(f::Tracker.var"#20#22"{DiffEqSensitivity.var"#26#30"{Float32, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}}, Tuple{TrackedArray{…,Vector{Float32}}, TrackedArray{…,ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}}}}, ps::Tracker.Params) @ Tracker C:\Users\ilyao\.julia\packages\Tracker\9xWLl\src\back.jl:135 [12] forward(::Function, ::Vector{Float32}, ::ComponentVector{Float32}) @ Tracker C:\Users\ilyao\.julia\packages\Tracker\9xWLl\src\back.jl:148 [13] _vecjacobian!(dλ::SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true}, y::Vector{Float32}, λ::SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true}, p::ComponentVector{Float32}, t::Float32, S::DiffEqSensitivity.ODEInterpolatingAdjointSensitivityFunction{DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, Vector{Float32}, ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, Nothing, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}}, isautojacvec::TrackerVJP, dgrad::SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true}, dy::Nothing, W::Nothing) @ DiffEqSensitivity C:\Users\ilyao\.julia\packages\DiffEqSensitivity\SjURy\src\derivative_wrappers.jl:317 [14] #vecjacobian!#25 @ C:\Users\ilyao\.julia\packages\DiffEqSensitivity\SjURy\src\derivative_wrappers.jl:224 [inlined] [15] (::DiffEqSensitivity.ODEInterpolatingAdjointSensitivityFunction{DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, Vector{Float32}, ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, Nothing, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}})(du::Vector{Float32}, u::Vector{Float32}, p::ComponentVector{Float32}, t::Float32) @ DiffEqSensitivity C:\Users\ilyao\.julia\packages\DiffEqSensitivity\SjURy\src\interpolating_adjoint.jl:116 [16] ODEFunction @ C:\Users\ilyao\.julia\packages\SciMLBase\dYFnI\src\scimlfunctions.jl:1595 [inlined] [17] initialize!(integrator::OrdinaryDiffEq.ODEIntegrator{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, true, Vector{Float32}, Nothing, Float32, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, Float32, Float32, Float32, Float32, Vector{Vector{Float32}}, ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, DiffEqSensitivity.ODEInterpolatingAdjointSensitivityFunction{DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, Vector{Float32}, ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, Nothing, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, CallbackSet{Tuple{}, Tuple{DiscreteCallback{DiffEqCallbacks.var"#60#63"{Vector{Float32}}, DiffEqCallbacks.var"#61#64"{DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, DiffEqCallbacks.var"#62#65"{typeof(SciMLBase.INITIALIZE_DEFAULT), Bool, Vector{Float32}, DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, typeof(SciMLBase.FINALIZE_DEFAULT)}}}, Tuple{Symbol}, NamedTuple{(:callback,), Tuple{CallbackSet{Tuple{}, Tuple{DiscreteCallback{DiffEqCallbacks.var"#60#63"{Vector{Float32}}, DiffEqCallbacks.var"#61#64"{DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, DiffEqCallbacks.var"#62#65"{typeof(SciMLBase.INITIALIZE_DEFAULT), Bool, Vector{Float32}, DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, typeof(SciMLBase.FINALIZE_DEFAULT)}}}}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, DiffEqSensitivity.ODEInterpolatingAdjointSensitivityFunction{DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, Vector{Float32}, ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, Nothing, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, ODEFunction{true, DiffEqSensitivity.ODEInterpolatingAdjointSensitivityFunction{DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, Vector{Float32}, ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, Nothing, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.DEOptions{Float64, Float64, Float32, Float32, PIController{Rational{Int64}}, typeof(DiffEqBase.ODE_DEFAULT_NORM), typeof(LinearAlgebra.opnorm), Nothing, CallbackSet{Tuple{}, Tuple{DiscreteCallback{DiffEqCallbacks.var"#60#63"{Vector{Float32}}, DiffEqCallbacks.var"#61#64"{DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, DiffEqCallbacks.var"#62#65"{typeof(SciMLBase.INITIALIZE_DEFAULT), Bool, Vector{Float32}, DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, typeof(SciMLBase.FINALIZE_DEFAULT)}}}, typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), DataStructures.BinaryHeap{Float32, DataStructures.FasterForward}, DataStructures.BinaryHeap{Float32, DataStructures.FasterForward}, Nothing, Nothing, Int64, Vector{Float32}, Vector{Float32}, Tuple{}}, Vector{Float32}, Float32, Nothing, OrdinaryDiffEq.DefaultInit}, cache::OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}) @ OrdinaryDiffEq C:\Users\ilyao\.julia\packages\OrdinaryDiffEq\ZgJ9s\src\perform_step\low_order_rk_perform_step.jl:627 [18] __init(prob::ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, DiffEqSensitivity.ODEInterpolatingAdjointSensitivityFunction{DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, Vector{Float32}, ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, Nothing, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, CallbackSet{Tuple{}, Tuple{DiscreteCallback{DiffEqCallbacks.var"#60#63"{Vector{Float32}}, DiffEqCallbacks.var"#61#64"{DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, DiffEqCallbacks.var"#62#65"{typeof(SciMLBase.INITIALIZE_DEFAULT), Bool, Vector{Float32}, DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, typeof(SciMLBase.FINALIZE_DEFAULT)}}}, Tuple{Symbol}, NamedTuple{(:callback,), Tuple{CallbackSet{Tuple{}, Tuple{DiscreteCallback{DiffEqCallbacks.var"#60#63"{Vector{Float32}}, DiffEqCallbacks.var"#61#64"{DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, DiffEqCallbacks.var"#62#65"{typeof(SciMLBase.INITIALIZE_DEFAULT), Bool, Vector{Float32}, DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, typeof(SciMLBase.FINALIZE_DEFAULT)}}}}}}, SciMLBase.StandardODEProblem}, alg::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, timeseries_init::Tuple{}, ts_init::Tuple{}, ks_init::Tuple{}, recompile::Type{Val{true}}; saveat::Vector{Float32}, tstops::Vector{Float32}, d_discontinuities::Tuple{}, save_idxs::Nothing, save_everystep::Bool, save_on::Bool, save_start::Bool, save_end::Nothing, callback::CallbackSet{Tuple{}, Tuple{DiscreteCallback{DiffEqCallbacks.var"#60#63"{Vector{Float32}}, DiffEqCallbacks.var"#61#64"{DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, DiffEqCallbacks.var"#62#65"{typeof(SciMLBase.INITIALIZE_DEFAULT), Bool, Vector{Float32}, DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, typeof(SciMLBase.FINALIZE_DEFAULT)}}}, dense::Bool, calck::Bool, dt::Float32, dtmin::Nothing, dtmax::Float32, force_dtmin::Bool, adaptive::Bool, gamma::Rational{Int64}, abstol::Float64, reltol::Float64, qmin::Rational{Int64}, qmax::Int64, qsteady_min::Int64, qsteady_max::Int64, beta1::Nothing, beta2::Nothing, qoldinit::Rational{Int64}, controller::Nothing, fullnormalize::Bool, failfactor::Int64, maxiters::Int64, internalnorm::typeof(DiffEqBase.ODE_DEFAULT_NORM), internalopnorm::typeof(LinearAlgebra.opnorm), isoutofdomain::typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), unstable_check::typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), verbose::Bool, timeseries_errors::Bool, dense_errors::Bool, advance_to_tstop::Bool, stop_at_next_tstop::Bool, initialize_save::Bool, progress::Bool, progress_steps::Int64, progress_name::String, progress_message::typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), userdata::Nothing, allow_extrapolation::Bool, initialize_integrator::Bool, alias_u0::Bool, alias_du0::Bool, initializealg::OrdinaryDiffEq.DefaultInit, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}) @ OrdinaryDiffEq C:\Users\ilyao\.julia\packages\OrdinaryDiffEq\ZgJ9s\src\solve.jl:456 [19] __solve(::ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, DiffEqSensitivity.ODEInterpolatingAdjointSensitivityFunction{DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, Vector{Float32}, ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, Nothing, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, CallbackSet{Tuple{}, Tuple{DiscreteCallback{DiffEqCallbacks.var"#60#63"{Vector{Float32}}, DiffEqCallbacks.var"#61#64"{DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, DiffEqCallbacks.var"#62#65"{typeof(SciMLBase.INITIALIZE_DEFAULT), Bool, Vector{Float32}, DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, typeof(SciMLBase.FINALIZE_DEFAULT)}}}, Tuple{Symbol}, NamedTuple{(:callback,), Tuple{CallbackSet{Tuple{}, Tuple{DiscreteCallback{DiffEqCallbacks.var"#60#63"{Vector{Float32}}, DiffEqCallbacks.var"#61#64"{DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, DiffEqCallbacks.var"#62#65"{typeof(SciMLBase.INITIALIZE_DEFAULT), Bool, Vector{Float32}, DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, typeof(SciMLBase.FINALIZE_DEFAULT)}}}}}}, SciMLBase.StandardODEProblem}, ::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}; kwargs::Base.Pairs{Symbol, Any, NTuple{7, Symbol}, NamedTuple{(:callback, :save_everystep, :save_start, :saveat, :tstops, :abstol, :reltol), Tuple{CallbackSet{Tuple{}, Tuple{DiscreteCallback{DiffEqCallbacks.var"#60#63"{Vector{Float32}}, DiffEqCallbacks.var"#61#64"{DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, DiffEqCallbacks.var"#62#65"{typeof(SciMLBase.INITIALIZE_DEFAULT), Bool, Vector{Float32}, DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, typeof(SciMLBase.FINALIZE_DEFAULT)}}}, Bool, Bool, Vector{Float32}, Vector{Float32}, Float64, Float64}}}) @ OrdinaryDiffEq C:\Users\ilyao\.julia\packages\OrdinaryDiffEq\ZgJ9s\src\solve.jl:4 [20] #solve_call#28 @ C:\Users\ilyao\.julia\packages\DiffEqBase\hZncn\src\solve.jl:428 [inlined] [21] solve_up(prob::ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, DiffEqSensitivity.ODEInterpolatingAdjointSensitivityFunction{DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, Vector{Float32}, ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, Nothing, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, CallbackSet{Tuple{}, Tuple{DiscreteCallback{DiffEqCallbacks.var"#60#63"{Vector{Float32}}, DiffEqCallbacks.var"#61#64"{DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, DiffEqCallbacks.var"#62#65"{typeof(SciMLBase.INITIALIZE_DEFAULT), Bool, Vector{Float32}, DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, typeof(SciMLBase.FINALIZE_DEFAULT)}}}, Tuple{Symbol}, NamedTuple{(:callback,), Tuple{CallbackSet{Tuple{}, Tuple{DiscreteCallback{DiffEqCallbacks.var"#60#63"{Vector{Float32}}, DiffEqCallbacks.var"#61#64"{DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, DiffEqCallbacks.var"#62#65"{typeof(SciMLBase.INITIALIZE_DEFAULT), Bool, Vector{Float32}, DiffEqSensitivity.ReverseLossCallback{Vector{Float32}, Vector{Float32}, Vector{Float32}, Base.RefValue{Int64}, LinearAlgebra.UniformScaling{Bool}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, DiffEqSensitivity.AdjointDiffCache{Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Vector{Float32}, Nothing, Base.OneTo{Int64}, UnitRange{Int64}, LinearAlgebra.UniformScaling{Bool}}}}, typeof(SciMLBase.FINALIZE_DEFAULT)}}}}}}, SciMLBase.StandardODEProblem}, sensealg::Nothing, u0::Vector{Float32}, p::ComponentVector{Float32}, args::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}; kwargs::Base.Pairs{Symbol, Any, NTuple{6, Symbol}, NamedTuple{(:save_everystep, :save_start, :saveat, :tstops, :abstol, :reltol), Tuple{Bool, Bool, Vector{Float32}, Vector{Float32}, Float64, Float64}}}) @ DiffEqBase C:\Users\ilyao\.julia\packages\DiffEqBase\hZncn\src\solve.jl:726 [22] #solve#29 @ C:\Users\ilyao\.julia\packages\DiffEqBase\hZncn\src\solve.jl:710 [inlined] [23] _adjoint_sensitivities(sol::ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, sensealg::InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, alg::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, g::DiffEqSensitivity.var"#df#236"{Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, Colon}, t::Vector{Float32}, dg::Nothing; abstol::Float64, reltol::Float64, checkpoints::Vector{Float32}, corfunc_analytical::Nothing, callback::Nothing, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}) @ DiffEqSensitivity C:\Users\ilyao\.julia\packages\DiffEqSensitivity\SjURy\src\sensitivity_interface.jl:305 [24] adjoint_sensitivities(::ODESolution{Float32, 2, Vector{Vector{Float32}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{Vector{Float32}}}, ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, true, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{true, typeof(dudt!), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{Vector{Float32}}, Vector{Float32}, Vector{Vector{Vector{Float32}}}, OrdinaryDiffEq.Tsit5Cache{Vector{Float32}, Vector{Float32}, Vector{Float32}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, DiffEqBase.DEStats}, ::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, ::Vararg{Any}; sensealg::InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, kwargs::Base.Pairs{Symbol, Nothing, Tuple{Symbol}, NamedTuple{(:callback,), Tuple{Nothing}}}) @ DiffEqSensitivity C:\Users\ilyao\.julia\packages\DiffEqSensitivity\SjURy\src\sensitivity_interface.jl:271 [25] (::DiffEqSensitivity.var"#adjoint_sensitivity_backpass#235"{Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, Vector{Float32}, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, Tuple{}, Colon, NamedTuple{(), Tuple{}}})(Δ::Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) @ DiffEqSensitivity C:\Users\ilyao\.julia\packages\DiffEqSensitivity\SjURy\src\concrete_solve.jl:253 [26] ZBack @ C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\compiler\chainrules.jl:205 [inlined] [27] (::Zygote.var"#kw_zpullback#37"{DiffEqSensitivity.var"#adjoint_sensitivity_backpass#235"{Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, Vector{Float32}, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, Tuple{}, Colon, NamedTuple{(), Tuple{}}}})(dy::Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) @ Zygote C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\compiler\chainrules.jl:231 [28] #208 @ C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\lib\lib.jl:207 [inlined] [29] (::Zygote.var"#1750#back#210"{Zygote.var"#208#209"{Tuple{NTuple{6, Nothing}, Tuple{Nothing}}, Zygote.var"#kw_zpullback#37"{DiffEqSensitivity.var"#adjoint_sensitivity_backpass#235"{Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, InterpolatingAdjoint{0, true, Val{:central}, TrackerVJP, Bool}, Vector{Float32}, ComponentVector{Float32, Vector{Float32}, Tuple{Axis{(layer_1 = ViewAxis(1:36, Axis(weight = ViewAxis(1:24, ShapedAxis((12, 2), NamedTuple())), bias = ViewAxis(25:36, ShapedAxis((12, 1), NamedTuple())))), layer_2 = ViewAxis(37:192, Axis(weight = ViewAxis(1:144, ShapedAxis((12, 12), NamedTuple())), bias = ViewAxis(145:156, ShapedAxis((12, 1), NamedTuple())))), layer_3 = ViewAxis(193:218, Axis(weight = ViewAxis(1:24, ShapedAxis((2, 12), NamedTuple())), bias = ViewAxis(25:26, ShapedAxis((2, 1), NamedTuple())))), layer_4 = 219:218)}}}, Tuple{}, Colon, NamedTuple{(), Tuple{}}}}}})(Δ::Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) @ Zygote C:\Users\ilyao\.julia\packages\ZygoteRules\AIbCs\src\adjoint.jl:67 [30] Pullback @ C:\Users\ilyao\.julia\packages\DiffEqBase\hZncn\src\solve.jl:710 [inlined] [31] (::typeof(∂(#solve#29)))(Δ::Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) @ Zygote C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0 [32] (::Zygote.var"#208#209"{Tuple{NTuple{6, Nothing}, Tuple{Nothing}}, typeof(∂(#solve#29))})(Δ::Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) @ Zygote C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\lib\lib.jl:207 [33] (::Zygote.var"#1750#back#210"{Zygote.var"#208#209"{Tuple{NTuple{6, Nothing}, Tuple{Nothing}}, typeof(∂(#solve#29))}})(Δ::Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) @ Zygote C:\Users\ilyao\.julia\packages\ZygoteRules\AIbCs\src\adjoint.jl:67 [34] Pullback @ C:\Users\ilyao\.julia\packages\DiffEqBase\hZncn\src\solve.jl:703 [inlined] [35] (::typeof(∂(solve##kw)))(Δ::Zygote.OneElement{Float32, 2, Tuple{Int64, Int64}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}) @ Zygote C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0 [36] Pullback @ .\REPL[49]:4 [inlined] [37] (::typeof(∂(loss)))(Δ::Float32) @ Zygote C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0 [38] Pullback @ .\REPL[41]:1 [inlined] [39] (::typeof(∂(loss)))(Δ::Float32) @ Zygote C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0 [40] (::Zygote.var"#52#53"{typeof(∂(loss))})(Δ::Float32) @ Zygote C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\compiler\interface.jl:41 [41] gradient(f::Function, args::ComponentVector{Float32}) @ Zygote C:\Users\ilyao\.julia\packages\Zygote\DkIUK\src\compiler\interface.jl:76 [42] top-level scope @ REPL[50]:1

using Lux, OrdinaryDiffEq, DiffEqSensitivity, Zygote, Statistics, Random, Test rng = Random.default_rng() function lotka!(du, u, p, t) α, β, γ, δ = p du[1] = α*u[1] - β*u[2]*u[1] du[2] = γ*u[1]*u[2] - δ*u[2] end tspan = (0.0f0,3.0f0) u0 = Float32[0.44249296,4.6280594] p_ = Float32[1.3, 0.9, 0.8, 1.8] prob = ODEProblem(lotka!, u0,tspan, p_) solution = solve(prob, Vern7(), abstol=1e-12, reltol=1e-12, saveat = 0.1) X = Array(solution) t = solution.t x̄ = mean(X, dims = 2) noise_magnitude = Float32(5e-2) Xₙ = X .+ (noise_magnitude*x̄) .* randn(eltype(X), size(X)) rbf(x) = exp.(-(x.^2)) U = Lux.Chain( Lux.Dense(2,5,rbf), Lux.Dense(5,5,rbf), Lux.Dense(5,5,rbf), Lux.Dense(5,2) ) p, st = Lux.setup(rng, U) p = Lux.ComponentArray(p) function ude_dynamics!(du,u, p, t, p_true) û = U(u, p, st)[1] du[1] = p_true[1]*u[1] + û[1] du[2] = -p_true[4]*u[2] + û[2] end nn_dynamics!(du,u,p,t) = ude_dynamics!(du,u,p,t,p_) prob_nn = ODEProblem(nn_dynamics!,Xₙ[:, 1], tspan, p) u0 = Xₙ[:,1] _sol = solve(prob_nn, Vern7(), u0 = u0, p=p, saveat = t, abstol=1e-8, reltol=1e-8, sensealg = ForwardDiffSensitivity() ) ū0,adj = adjoint_sensitivities(_sol,Vern7(),((out,u,p,t,i) -> out .= -1),tspan,abstol=1e-8,reltol=1e-8) du01,dp1 = Zygote.gradient((u0,p)->sum(solve(prob_nn,Vern7(),u0=u0,p=p,abstol=1e-8,reltol=1e-8,saveat=t,sensealg=QuadratureAdjoint())),u0,p) du02,dp2 = Zygote.gradient((u0,p)->sum(solve(prob_nn,Vern7(),u0=u0,p=p,abstol=1e-8,reltol=1e-8,saveat=t,sensealg=InterpolatingAdjoint())),u0,p)

using Lux, ComponentArrays, Random, Tracker c = Chain(Dense(3, 2), Dense(2, 1)) x = randn(Float32, 3, 1) ps, st = Lux.setup(Random.default_rng(), c) ps_c = ps |> Lux.ComponentArray Tracker.param(xs::ComponentArray) = ComponentArray(TrackedArray(float.(getdata(xs))), getaxes(xs)) Tracker.gradient(ps -> sum(first(Lux.apply(c, x, ps, st))), ps_c)

ERROR: MethodError: Cannot `convert` an object of type Vector{Float32} to an object of type SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true} Closest candidates are: convert(::Type{T}, ::LinearAlgebra.Factorization) where T<:AbstractArray at /mnt/softwares/julia-nightly/share/julia/stdlib/v1.8/LinearAlgebra/src/factorization.jl:58 convert(::Type{T}, ::T) where T<:AbstractArray at abstractarray.jl:16 convert(::Type{T}, ::T) where T at Base.jl:61 ... Stacktrace: [1] setproperty!(x::Tracker.Tracked{SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true}}, f::Symbol, v::Vector{Float32}) @ Base ./Base.jl:39 [2] back(x::Tracker.Tracked{SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true}}, Δ::Vector{Float32}, once::Bool) @ Tracker /mnt/julia/packages/Tracker/9xWLl/src/back.jl:53 [3] #13 @ /mnt/julia/packages/Tracker/9xWLl/src/back.jl:38 [inlined] [4] #58 @ ./tuple.jl:556 [inlined] [5] BottomRF @ ./reduce.jl:81 [inlined] [6] _foldl_impl(op::Base.BottomRF{Base.var"#58#59"{Tracker.var"#13#14"{Bool}}}, init::Nothing, itr::Base.Iterators.Zip{Tuple{Tuple{Tracker.Tracked{SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true}}, Nothing}, Tuple{Vector{Float32}, Nothing}}}) @ Base ./reduce.jl:58 [7] foldl_impl @ ./reduce.jl:48 [inlined] [8] mapfoldl_impl @ ./reduce.jl:44 [inlined] [9] #mapfoldl#258 @ ./reduce.jl:162 [inlined] [10] #foldl#259 @ ./reduce.jl:185 [inlined] [11] foreach @ ./tuple.jl:556 [inlined] [12] back_(c::Tracker.Call{Tracker.var"#442#444"{Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, TrackedArray{…,SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true}}, Tuple{UnitRange{Int64}}}, Tuple{Tracker.Tracked{SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true}}, Nothing}}, Δ::Vector{Float32}, once::Bool) @ Tracker /mnt/julia/packages/Tracker/9xWLl/src/back.jl:38 [13] back(x::Tracker.Tracked{SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true}}, Δ::Vector{Float32}, once::Bool) @ Tracker /mnt/julia/packages/Tracker/9xWLl/src/back.jl:58 --- the last 11 lines are repeated 2 more times --- [36] #13 @ /mnt/julia/packages/Tracker/9xWLl/src/back.jl:38 [inlined] [37] #58 @ ./tuple.jl:556 [inlined] [38] BottomRF @ ./reduce.jl:81 [inlined] [39] _foldl_impl(op::Base.BottomRF{Base.var"#58#59"{Tracker.var"#13#14"{Bool}}}, init::Nothing, itr::Base.Iterators.Zip{Tuple{Tuple{Tracker.Tracked{Matrix{Float32}}, Tracker.Tracked{Base.ReshapedArray{Float32, 2, SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true}, Tuple{}}}}, Tuple{Matrix{Float32}, Matrix{Float32}}}}) @ Base ./reduce.jl:58 [40] foldl_impl @ ./reduce.jl:48 [inlined] [41] mapfoldl_impl(f::typeof(identity), op::Base.var"#58#59"{Tracker.var"#13#14"{Bool}}, nt::Nothing, itr::Base.Iterators.Zip{Tuple{Tuple{Tracker.Tracked{Matrix{Float32}}, Tracker.Tracked{Base.ReshapedArray{Float32, 2, SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true}, Tuple{}}}}, Tuple{Matrix{Float32}, Matrix{Float32}}}}) @ Base ./reduce.jl:44 [42] mapfoldl(f::Function, op::Function, itr::Base.Iterators.Zip{Tuple{Tuple{Tracker.Tracked{Matrix{Float32}}, Tracker.Tracked{Base.ReshapedArray{Float32, 2, SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true}, Tuple{}}}}, Tuple{Matrix{Float32}, Matrix{Float32}}}}; init::Nothing) @ Base ./reduce.jl:162 [43] #foldl#259 @ ./reduce.jl:185 [inlined] [44] foreach(::Function, ::Tuple{Tracker.Tracked{Matrix{Float32}}, Tracker.Tracked{Base.ReshapedArray{Float32, 2, SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true}, Tuple{}}}}, ::Tuple{Matrix{Float32}, Matrix{Float32}}) @ Base ./tuple.jl:556 [45] back_(c::Tracker.Call{Tracker.var"#back#622"{2, typeof(+), Tuple{TrackedArray{…,Matrix{Float32}}, TrackedArray{…,Base.ReshapedArray{Float32, 2, SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true}, Tuple{}}}}}, Tuple{Tracker.Tracked{Matrix{Float32}}, Tracker.Tracked{Base.ReshapedArray{Float32, 2, SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true}, Tuple{}}}}}, Δ::Matrix{Float32}, once::Bool) @ Tracker /mnt/julia/packages/Tracker/9xWLl/src/back.jl:38 [46] back(x::Tracker.Tracked{Matrix{Float32}}, Δ::Matrix{Float32}, once::Bool) @ Tracker /mnt/julia/packages/Tracker/9xWLl/src/back.jl:58 [47] #13 @ /mnt/julia/packages/Tracker/9xWLl/src/back.jl:38 [inlined] [48] #58 @ ./tuple.jl:556 [inlined] [49] BottomRF @ ./reduce.jl:81 [inlined] [50] _foldl_impl @ ./reduce.jl:58 [inlined] [51] foldl_impl @ ./reduce.jl:48 [inlined] [52] mapfoldl_impl @ ./reduce.jl:44 [inlined] [53] #mapfoldl#258 @ ./reduce.jl:162 [inlined] [54] #foldl#259 @ ./reduce.jl:185 [inlined] [55] foreach @ ./tuple.jl:556 [inlined] [56] back_(c::Tracker.Call{Tracker.var"#552#553"{TrackedArray{…,Matrix{Float32}}}, Tuple{Tracker.Tracked{Matrix{Float32}}}}, Δ::Float32, once::Bool) @ Tracker /mnt/julia/packages/Tracker/9xWLl/src/back.jl:38 [57] back(x::Tracker.Tracked{Float32}, Δ::Int64, once::Bool) @ Tracker /mnt/julia/packages/Tracker/9xWLl/src/back.jl:58 [58] #back!#15 @ /mnt/julia/packages/Tracker/9xWLl/src/back.jl:77 [inlined] [59] #back!#32 @ /mnt/julia/packages/Tracker/9xWLl/src/lib/real.jl:16 [inlined] [60] back!(x::Tracker.TrackedReal{Float32}) @ Tracker /mnt/julia/packages/Tracker/9xWLl/src/lib/real.jl:14 [61] gradient_(f::Function, xs::ComponentVector{Float32}) @ Tracker /mnt/julia/packages/Tracker/9xWLl/src/back.jl:4 [62] gradient(f::Function, xs::ComponentVector{Float32}; nest::Bool) @ Tracker /mnt/julia/packages/Tracker/9xWLl/src/back.jl:164 [63] gradient(f::Function, xs::ComponentVector{Float32}) @ Tracker /mnt/julia/packages/Tracker/9xWLl/src/back.jl:164 [64] top-level scope @ REPL[33]:1

julia> grads = Zygote.gradient((x,p,st1,st2) -> sum(andsde(x,p,st1,st2)[1]),x,p,st1,st2) ERROR: MethodError: no method matching zero(::Type{ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:250, Axis(weight = ViewAxis(1:200, ShapedAxis((50, 4), NamedTuple())), bias = ViewAxis(201:250, ShapedAxis((50, 1), NamedTuple())))), layer_2 = ViewAxis(251:454, Axis(weight = ViewAxis(1:200, ShapedAxis((4, 50), NamedTuple())), bias = ViewAxis(201:204, ShapedAxis((4, 1), NamedTuple())))))}}}}) Closest candidates are: zero(::Union{Type{P}, P}) where P<:Dates.Period at C:\Users\user\AppData\Local\Programs\Julia-1.7.2\share\julia\stdlib\v1.7\Dates\src\periods.jl:53 zero(::Union{AbstractAlgebra.Generic.LaurentSeriesFieldElem{T}, AbstractAlgebra.Generic.LaurentSeriesRingElem{T}} where T<:AbstractAlgebra.RingElement) at C:\Users\user\.julia\packages\AbstractAlgebra\nmiq9\src\generic\LaurentSeries.jl:466 zero(::Union{AbstractAlgebra.Generic.LaurentSeriesFieldElem{T}, AbstractAlgebra.Generic.LaurentSeriesRingElem{T}} where T<:AbstractAlgebra.RingElement, ::String; cached) at C:\Users\user\.julia\packages\AbstractAlgebra\nmiq9\src\generic\LaurentSeries.jl:480

The issue here, as I said above is ReverseDiffAdjoint wrapping the second argument

ERROR: MethodError: no method matching (::Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}})(::ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ::ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentArrays.ComponentVector{Float32, SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true}, Tuple{ComponentArrays.Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))}}}, ComponentArrays.ComponentVector{Float32, SubArray{Float32, 1, Vector{Float32}, Tuple{UnitRange{Int64}}, true},
Tuple{ComponentArrays.Axis{(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))}}}}, ::NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}})
Closest candidates are:
  (::Lux.Chain)(::Any, !Matched::Union{ComponentArrays.ComponentArray, NamedTuple}, ::NamedTuple) at C:\Users\user\.julia\packages\Lux\qQlb5\src\layers\basic.jl:519

Stacktrace:

(::DiffEqFlux.var"#dudt_#150"{DiffEqFlux.var"#dudt_#148#151"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity),
typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}})(u::ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, p::ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}}, t::Float32; st::NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}) at [C:\Users\user.julia\dev\DiffEqFlux\src\neural_de.jl](vscode-file://vscode-app/c:/Program%20Files/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-browser/workbench/workbench.html)

(::DiffEqFlux.var"#dudt_#150"{DiffEqFlux.var"#dudt_#148#151"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}})(u::ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, p::ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}}, t::Float32) at [C:\Users\user.julia\dev\DiffEqFlux\src\neural_de.jl](vscode-file://vscode-app/c:/Program%20Files/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-browser/workbench/workbench.html)

(::SDEFunction{false, DiffEqFlux.var"#dudt_#150"{DiffEqFlux.var"#dudt_#148#151"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing})(::ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ::Vararg{Any}) at [C:\Users\user.julia\packages\SciMLBase\dYFnI\src\scimlfunctions.jl](vscode-file://vscode-app/c:/Program%20Files/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-browser/workbench/workbench.html)

(::DiffEqSensitivity.var"#_f#281"{SDEProblem{Matrix{Float32}, Tuple{Float32, Float32}, false, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, Nothing, SDEFunction{false, DiffEqFlux.var"#dudt_#150"{DiffEqFlux.var"#dudt_#148#151"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}})(::ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ::Vararg{Any}) at [C:\Users\user.julia\packages\DiffEqSensitivity\kMyur\src\concrete_solve.jl](vscode-file://vscode-app/c:/Program%20Files/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-browser/workbench/workbench.html)

(::SDEFunction{false, DiffEqSensitivity.var"#_f#281"{SDEProblem{Matrix{Float32}, Tuple{Float32, Float32}, false, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, Nothing, SDEFunction{false, DiffEqFlux.var"#dudt_#150"{DiffEqFlux.var"#dudt_#148#151"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}}, DiffEqSensitivity.var"#_g#282"{SDEProblem{Matrix{Float32}, Tuple{Float32, Float32}, false, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, Nothing, SDEFunction{false, DiffEqFlux.var"#dudt_#150"{DiffEqFlux.var"#dudt_#148#151"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing})(::ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ::Vararg{Any}) at [C:\Users\user.julia\packages\SciMLBase\dYFnI\src\scimlfunctions.jl](vscode-file://vscode-app/c:/Program%20Files/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-browser/workbench/workbench.html)

sde_determine_initdt(u0::ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, t::Float32, tdir::Float32, dtmax::Float32, abstol::Float64, reltol::Float64, internalnorm::typeof(DiffEqBase.ODE_DEFAULT_NORM), prob::SDEProblem{ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, Tuple{Float32, Float32}, false, ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}}, Nothing, SDEFunction{false, DiffEqSensitivity.var"#_f#281"{SDEProblem{Matrix{Float32}, Tuple{Float32, Float32}, false, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, Nothing, SDEFunction{false, DiffEqFlux.var"#dudt_#150"{DiffEqFlux.var"#dudt_#148#151"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}}, DiffEqSensitivity.var"#_g#282"{SDEProblem{Matrix{Float32}, Tuple{Float32, Float32}, false, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, Nothing, SDEFunction{false, DiffEqFlux.var"#dudt_#150"{DiffEqFlux.var"#dudt_#148#151"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqSensitivity.var"#_g#282"{SDEProblem{Matrix{Float32}, Tuple{Float32, Float32}, false, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, Nothing, SDEFunction{false, DiffEqFlux.var"#dudt_#150"{DiffEqFlux.var"#dudt_#148#151"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}, order::Rational{Int64}, integrator::StochasticDiffEq.SDEIntegrator{SOSRI, false, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}}, Float32, Float32, ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, DiffEqNoiseProcess.NoiseProcess{ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, 3, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, Vector{ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, typeof(DiffEqNoiseProcess.WHITE_NOISE_DIST), typeof(DiffEqNoiseProcess.WHITE_NOISE_BRIDGE), false, ResettableStacks.ResettableStack{Tuple{Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, false}, ResettableStacks.ResettableStack{Tuple{Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, false}, DiffEqNoiseProcess.RSWM{Float64}, Nothing, RandomNumbers.Xorshifts.Xoroshiro128Plus}, Nothing, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, RODESolution{ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, 3, Vector{ReverseDiff.TrackedArray{Float32, 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typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), 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ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, Nothing, SDEFunction{false, DiffEqFlux.var"#dudt_#150"{DiffEqFlux.var"#dudt_#148#151"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqSensitivity.var"#_g#282"{SDEProblem{Matrix{Float32}, Tuple{Float32, Float32}, false, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, Nothing, SDEFunction{false, DiffEqFlux.var"#dudt_#150"{DiffEqFlux.var"#dudt_#148#151"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}}, Nothing, StochasticDiffEq.SDEOptions{Float32, Float32, OrdinaryDiffEq.PIController{Float32}, typeof(DiffEqBase.ODE_DEFAULT_NORM), Nothing, CallbackSet{Tuple{}, Tuple{}}, typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), DataStructures.BinaryHeap{Float32, DataStructures.FasterForward}, DataStructures.BinaryHeap{Float32, DataStructures.FasterForward}, Nothing, Nothing, Int64, Float64, Float64, ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, Tuple{}, StepRangeLen{Float32, Float64, Float64, Int64}, Tuple{}}, Nothing, ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, Nothing, Nothing}) at [C:\Users\user.julia\packages\StochasticDiffEq\LYyNp\src\initdt.jl](vscode-file://vscode-app/c:/Program%20Files/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-browser/workbench/workbench.html)

`auto_dt_reset!(integrator::StochasticDiffEq.SDEIntegrator{SOSRI, false, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}}, Float32, Float32, ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, DiffEqNoiseProcess.NoiseProcess{ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, 3, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, Vector{ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, typeof(DiffEqNoiseProcess.WHITE_NOISE_DIST), typeof(DiffEqNoiseProcess.WHITE_NOISE_BRIDGE), false, ResettableStacks.ResettableStack{Tuple{Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, false}, ResettableStacks.ResettableStack{Tuple{Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, false}, DiffEqNoiseProcess.RSWM{Float64}, Nothing, RandomNumbers.Xorshifts.Xoroshiro128Plus}, Nothing, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, RODESolution{ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, 3, Vector{ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, Nothing, Nothing, Vector{Float32}, DiffEqNoiseProcess.NoiseProcess{ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, 3, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, Vector{ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, typeof(DiffEqNoiseProcess.WHITE_NOISE_DIST), typeof(DiffEqNoiseProcess.WHITE_NOISE_BRIDGE), false, ResettableStacks.ResettableStack{Tuple{Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, false}, ResettableStacks.ResettableStack{Tuple{Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, false}, DiffEqNoiseProcess.RSWM{Float64}, Nothing, RandomNumbers.Xorshifts.Xoroshiro128Plus}, SDEProblem{ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, Tuple{Float32, Float32}, false, ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}}, Nothing, SDEFunction{false, DiffEqSensitivity.var"#f#281"{SDEProblem{Matrix{Float32}, Tuple{Float32, Float32}, false, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, Nothing, SDEFunction{false, DiffEqFlux.var"#dudt#150"{DiffEqFlux.var"#dudt_#148#151"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), 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typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol,…

(@v1.7) pkg> st Status `C:\Users\user\.julia\environments\v1.7\Project.toml` [4fba245c] ArrayInterface v6.0.17 [6e4b80f9] BenchmarkTools v1.3.1 [479239e8] Catalyst v12.0.0 [b0b7db55] ComponentArrays v0.12.0 [2445eb08] DataDrivenDiffEq v0.8.4 [bcd4f6db] DelayDiffEq v5.37.0 [f3b72e0c] DiffEqDevTools v2.30.0 [aae7a2af] DiffEqFlux v1.50.0 [9fdde737] DiffEqOperators v4.43.1 [41bf760c] DiffEqSensitivity v6.80.0 `C:\Users\user\.julia\dev\DiffEqSensitivity` [0c46a032] DifferentialEquations v7.1.0 [b4f34e82] Distances v0.10.7 [31c24e10] Distributions v0.25.62 [ced4e74d] DistributionsAD v0.6.41 [7da242da] Enzyme v0.10.1 [5789e2e9] FileIO v1.14.0 [587475ba] Flux v0.13.3 [f67ccb44] HDF5 v0.16.10 [033835bb] JLD2 v0.4.22 [98e50ef6] JuliaFormatter v1.0.3 [e5e0dc1b] Juno v0.8.4 [7f56f5a3] LSODA v0.7.0 [bdcacae8] LoopVectorization v0.12.118 [b2108857] Lux v0.4.6 [23992714] MAT v0.10.3 [eb30cadb] MLDatasets v0.7.2 [ee78f7c6] Makie v0.17.7 [961ee093] ModelingToolkit v8.14.1 [54ca160b] ODEInterface v0.5.0 [09606e27] ODEInterfaceDiffEq v3.10.1 [5913d0e6] OperatorLearning v0.2.2 `C:\Users\user\.julia\dev\OperatorLearning` [429524aa] Optim v1.7.0 [7f7a1694] Optimization v3.7.0 [253f991c] OptimizationFlux v0.1.0 [4e6fcdb7] OptimizationNLopt v0.1.0 [36348300] OptimizationOptimJL v0.1.1 [42dfb2eb] OptimizationOptimisers v0.1.0 [500b13db] OptimizationPolyalgorithms v0.1.0 [1dea7af3] OrdinaryDiffEq v6.16.1 [91a5bcdd] Plots v1.30.1 [b4db0fb7] ReactionNetworkImporters v0.13.4 [0bca4576] SciMLBase v1.41.3 [de6bee2f] SimpleChains v0.2.12 `C:\Users\user\.julia\dev\SimpleChains` [789caeaf] StochasticDiffEq v6.49.1 [c3572dad] Sundials v4.9.4 [a759f4b9] TimerOutputs v0.5.20 [3d5dd08c] VectorizationBase v0.21.36 [e88e6eb3] Zygote v0.6.40

ERROR: DimensionMismatch("arrays could not be broadcast to a common size; got a dimension with lengths 20 and 2") Stacktrace: _bcs1 at [.\broadcast.jl](vscode-file://vscode-app/c:/Program%20Files/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-browser/workbench/workbench.html) _bcs at [.\broadcast.jl](vscode-file://vscode-app/c:/Program%20Files/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-browser/workbench/workbench.html) broadcast_shape at [.\broadcast.jl](vscode-file://vscode-app/c:/Program%20Files/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-browser/workbench/workbench.html) combine_axes at [.\broadcast.jl](vscode-file://vscode-app/c:/Program%20Files/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-browser/workbench/workbench.html) instantiate at [.\broadcast.jl](vscode-file://vscode-app/c:/Program%20Files/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-browser/workbench/workbench.html) materialize(bc::Base.Broadcast.Broadcasted{ReverseDiff.TrackedStyle, Nothing, typeof(/), Tuple{Base.Broadcast.Broadcasted{ReverseDiff.TrackedStyle, Nothing, typeof(max), Tuple{Base.Broadcast.Broadcasted{ReverseDiff.TrackedStyle, Nothing, typeof(DiffEqBase.ODE_DEFAULT_NORM), Tuple{Base.Broadcast.Broadcasted{ReverseDiff.TrackedStyle, Nothing, typeof(+), Tuple{ReverseDiff.TrackedArray{Float32, Float32, 1, Vector{Float32}, Vector{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 1, Vector{Float32}, Vector{Float32}}}}, Float32}}, Base.Broadcast.Broadcasted{ReverseDiff.TrackedStyle, Nothing, typeof(DiffEqBase.ODE_DEFAULT_NORM), Tuple{Base.Broadcast.Broadcasted{ReverseDiff.TrackedStyle, Nothing, typeof(-), Tuple{ReverseDiff.TrackedArray{Float32, Float32, 1, Vector{Float32}, Vector{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 1, Vector{Float32}, Vector{Float32}}}}, Float32}}}}, Base.Broadcast.Broadcasted{ReverseDiff.TrackedStyle, Nothing, typeof(+), Tuple{Float64, Base.Broadcast.Broadcasted{ReverseDiff.TrackedStyle, Nothing, typeof(*), Tuple{Base.Broadcast.Broadcasted{ReverseDiff.TrackedStyle, Nothing, typeof(DiffEqBase.ODE_DEFAULT_NORM), Tuple{ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, Float32}}, Float64}}}}}}) at [.\broadcast.jl](vscode-file://vscode-app/c:/Program%20Files/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-browser/workbench/workbench.html) sde_determine_initdt(u0::ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, t::Float32, tdir::Float32, dtmax::Float32, abstol::Float64, reltol::Float64, internalnorm::typeof(DiffEqBase.ODE_DEFAULT_NORM), prob::SDEProblem{ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, Tuple{Float32, Float32}, false, ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}}, Nothing, SDEFunction{false, DiffEqSensitivity.var"#_f#281"{SDEProblem{Matrix{Float32}, Tuple{Float32, Float32}, false, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, Nothing, SDEFunction{false, DiffEqFlux.var"#dudt_#150"{DiffEqFlux.var"#dudt_#148#151"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}}, DiffEqSensitivity.var"#_g#282"{SDEProblem{Matrix{Float32}, Tuple{Float32, Float32}, false, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, Nothing, SDEFunction{false, DiffEqFlux.var"#dudt_#150"{DiffEqFlux.var"#dudt_#148#151"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqSensitivity.var"#_g#282"{SDEProblem{Matrix{Float32}, Tuple{Float32, Float32}, false, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, Nothing, SDEFunction{false, DiffEqFlux.var"#dudt_#150"{DiffEqFlux.var"#dudt_#148#151"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}, order::Rational{Int64}, integrator::StochasticDiffEq.SDEIntegrator{SOSRI, false, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), 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DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, 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NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqSensitivity.var"#_g#282"{SDEProblem{Matrix{Float32}, Tuple{Float32, Float32}, false, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), 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typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}}, Nothing, StochasticDiffEq.SDEOptions{Float32, Float32, OrdinaryDiffEq.PIController{Float32}, typeof(DiffEqBase.ODE_DEFAULT_NORM), Nothing, CallbackSet{Tuple{}, Tuple{}}, typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), DataStructures.BinaryHeap{Float32, DataStructures.FasterForward}, DataStructures.BinaryHeap{Float32, DataStructures.FasterForward}, Nothing, Nothing, Int64, Float64, Float64, ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, Tuple{}, StepRangeLen{Float32, Float64, Float64, Int64}, Tuple{}}, Nothing, ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, Nothing, Nothing}) at [C:\Users\user.julia\packages\StochasticDiffEq\LYyNp\src\initdt.jl](vscode-file://vscode-app/c:/Program%20Files/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-browser/workbench/workbench.html) auto_dt_reset!(integrator::StochasticDiffEq.SDEIntegrator{SOSRI, false, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}}, Float32, Float32, ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, DiffEqNoiseProcess.NoiseProcess{ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, 3, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, Vector{ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, typeof(DiffEqNoiseProcess.WHITE_NOISE_DIST), typeof(DiffEqNoiseProcess.WHITE_NOISE_BRIDGE), false, ResettableStacks.ResettableStack{Tuple{Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, false}, ResettableStacks.ResettableStack{Tuple{Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, false}, DiffEqNoiseProcess.RSWM{Float64}, Nothing, RandomNumbers.Xorshifts.Xoroshiro128Plus}, Nothing, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, RODESolution{ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, 3, Vector{ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, Nothing, Nothing, Vector{Float32}, DiffEqNoiseProcess.NoiseProcess{ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, 3, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, Vector{ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, typeof(DiffEqNoiseProcess.WHITE_NOISE_DIST), typeof(DiffEqNoiseProcess.WHITE_NOISE_BRIDGE), false, ResettableStacks.ResettableStack{Tuple{Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, false}, ResettableStacks.ResettableStack{Tuple{Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, false}, DiffEqNoiseProcess.RSWM{Float64}, Nothing, RandomNumbers.Xorshifts.Xoroshiro128Plus}, SDEProblem{ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, Tuple{Float32, Float32}, false, ReverseDiff.TrackedArray{Float32, Float32, 1, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}}, Nothing, SDEFunction{false, DiffEqSensitivity.var"#_f#281"{SDEProblem{Matrix{Float32}, Tuple{Float32, Float32}, false, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, 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typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, 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Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}}, Nothing, StochasticDiffEq.SDEOptions{Float32, Float32, OrdinaryDiffEq.PIController{Float32}, typeof(DiffEqBase.ODE_DEFAULT_NORM), Nothing, CallbackSet{Tuple{}, Tuple{}}, typeof(DiffEqBase.ODE_DEFAULT_ISOUTOFDOMAIN), typeof(DiffEqBase.ODE_DEFAULT_PROG_MESSAGE), typeof(DiffEqBase.ODE_DEFAULT_UNSTABLE_CHECK), DataStructures.BinaryHeap{Float32, DataStructures.FasterForward}, DataStructures.BinaryHeap{Float32, DataStructures.FasterForward}, Nothing, Nothing, Int64, Float64, Float64, ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, Tuple{}, StepRangeLen{Float32, Float64, Float64, Int64}, Tuple{}}, Nothing, ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, Nothing, Nothing}) at [C:\Users\user.julia\packages\StochasticDiffEq\LYyNp\src\integrators\integrator_interface.jl](vscode-file://vscode-app/c:/Program%20Files/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-browser/workbench/workbench.html) `handle_dt!(integrator::StochasticDiffEq.SDEIntegrator{SOSRI, false, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, 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= ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}}, Float32, Float32, ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, DiffEqNoiseProcess.NoiseProcess{ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, 3, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, Vector{ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, typeof(DiffEqNoiseProcess.WHITE_NOISE_DIST), typeof(DiffEqNoiseProcess.WHITE_NOISE_BRIDGE), false, ResettableStacks.ResettableStack{Tuple{Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, false}, ResettableStacks.ResettableStack{Tuple{Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, false}, DiffEqNoiseProcess.RSWM{Float64}, Nothing, RandomNumbers.Xorshifts.Xoroshiro128Plus}, Nothing, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}, RODESolution{ReverseDiff.TrackedReal{Float32, Float32, ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, 3, Vector{ReverseDiff.TrackedArray{Float32, Float32, 2, Matrix{Float32}, Matrix{Float32}}}, Nothing, Nothing, Vector{Float32}, DiffEqNoiseProcess.NoiseProcess{ReverseDiff.TrackedReal{Float32, 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Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqFlux.var"#g#152"{DiffEqFlux.var"#g#149#153"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Tuple{}}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Optimisers.Restructure{Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Tuple{}}, Tuple{Float32, Float32}, Tuple{SOSRI}, Base.Pairs{Symbol, Any, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:saveat, :reltol, :abstol), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}, Float64, Float64}}}}}, NamedTuple{(), Tuple{}}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, Nothing}}, LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, DiffEqSensitivity.var"#g#282"{SDEProblem{Matrix{Float32}, Tuple{Float32, Float32}, false, ComponentArrays.ComponentVector{Float32, Vector{Float32}, Tuple{ComponentArrays.Axis{(p1 = ViewAxis(1:252, Axis(layer_1 = 1:0, layer_2 = ViewAxis(1:150, Axis(weight = ViewAxis(1:100, ShapedAxis((50, 2), NamedTuple())), bias = ViewAxis(101:150, ShapedAxis((50, 1), NamedTuple())))), layer_3 = ViewAxis(151:252, Axis(weight = ViewAxis(1:100, ShapedAxis((2, 50), NamedTuple())), bias = ViewAxis(101:102, ShapedAxis((2, 1), NamedTuple())))))), p2 = ViewAxis(253:258, Axis(weight = ViewAxis(1:4, ShapedAxis((2, 2), NamedTuple())), bias = ViewAxis(5:6, ShapedAxis((2, 1), NamedTuple())))))}}}, Nothing, SDEFunction{false, DiffEqFlux.var"#dudt#150"{DiffEqFlux.var"#dudt_#148#151"{NeuralDSDE{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}, Lux.Dense{true, typeof(tanh_fast), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}, Lux.Dense{true, typeof(identity), typeof(Lux.glorot_uniform), typeof(Lux.zeros32)}}}}, Vector{Bool}, Optimisers.Restructure{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{ActivationFunction{var"#5#6"}…

Tracker.param(c::ComponentArray) = ComponentArray(Tracker.param(getdata(c)), getaxes(c)) Tracker.grad(c::ComponentArray) = Tracker.grad(getdata(c)) Tracker.tracker(c::ComponentArray) = Tracker.tracker(getdata(c))

SciML / SciMLSensitivity.jl

Lux does not handle sensitivity algorithms with ReverseDiff #609