NeuralODE example working on cpu and gpu

[CarloLucibello]

No description provided.

[YichengDWu]

It is not working on gpu on my computer

ERROR: type Tuple has no field layers
Stacktrace:
  [1] getproperty
    @ .\Base.jl:42 [inlined]
  [2] functor(#unused#::Type{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}}, c::Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}})
    @ GraphNeuralNetworks C:\Users\Luffy\.julia\packages\GraphNeuralNetworks\4e2gU\src\layers\basic.jl:117
  [3] _trainable_biwalk(f::Function, x::GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, aux::Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}})
    @ Optimisers C:\Users\Luffy\.julia\packages\Optimisers\jjON8\src\destructure.jl:95
  [4] #fmap#30
    @ C:\Users\Luffy\.julia\packages\Functors\qBIlC\src\functor.jl:78 [inlined]
  [5] _rebuild(x::GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, off::Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}}, flat::CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, len::Int64; walk::Function, kw::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ Optimisers C:\Users\Luffy\.julia\packages\Optimisers\jjON8\src\destructure.jl:84
  [6] _rebuild
    @ C:\Users\Luffy\.julia\packages\Optimisers\jjON8\src\destructure.jl:83 [inlined]
  [7] Restructure
    @ C:\Users\Luffy\.julia\packages\Optimisers\jjON8\src\destructure.jl:51 [inlined]
  [8] (::GraphNeuralNetworks.var"#re_withgraph#4"{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, Optimisers.Restructure{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}}}})(x::CuArray{Float32, 1, CUDA.Mem.DeviceBuffer})
    @ GraphNeuralNetworks C:\Users\Luffy\.julia\packages\GraphNeuralNetworks\4e2gU\src\layers\basic.jl:60
  [9] (::DiffEqFlux.var"#dudt_#133"{NeuralODE{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, GraphNeuralNetworks.var"#re_withgraph#4"{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, Optimisers.Restructure{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}}}}, Tuple{Float32, Float32}, Tuple{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float64, Float64, Bool}}}}})(u::CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, p::CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, t::Float32)
    @ DiffEqFlux C:\Users\Luffy\.julia\packages\DiffEqFlux\JicEk\src\neural_de.jl:73
 [10] ODEFunction
    @ C:\Users\Luffy\.julia\packages\SciMLBase\UEAKN\src\scimlfunctions.jl:1613 [inlined]
 [11] initialize!(integrator::OrdinaryDiffEq.ODEIntegrator{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, false, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, Nothing, Float32, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, Float32, Float32, Float32, Float32, Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, ODESolution{Float32, 3, Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Nothing, Nothing, Vector{Float32}, Vector{Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}, ODEProblem{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, Tuple{Float32, Float32}, false, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, ODEFunction{false, DiffEqFlux.var"#dudt_#133"{NeuralODE{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, GraphNeuralNetworks.var"#re_withgraph#4"{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, Optimisers.Restructure{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}}}}, Tuple{Float32, Float32}, Tuple{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float64, Float64, Bool}}}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), 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{false, DiffEqFlux.var"#dudt_#133"{NeuralODE{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, GraphNeuralNetworks.var"#re_withgraph#4"{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, Optimisers.Restructure{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}}}}, Tuple{Float32, Float32}, Tuple{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float64, Float64, Bool}}}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Vector{Float32}, Vector{Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}}, DiffEqBase.DEStats}, ODEFunction{false, DiffEqFlux.var"#dudt_#133"{NeuralODE{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, GraphNeuralNetworks.var"#re_withgraph#4"{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, Optimisers.Restructure{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}}}}, Tuple{Float32, Float32}, Tuple{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float64, Float64, Bool}}}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, OrdinaryDiffEq.DEOptions{Float64, Float64, Float32, Float32, PIController{Rational{Int64}}, typeof(DiffEqBase.ODE_DEFAULT_NORM), typeof(LinearAlgebra.opnorm), Bool, 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, Tuple{}, Tuple{}, Tuple{}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, Float32, Nothing, OrdinaryDiffEq.DefaultInit}, cache::OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32})
    @ OrdinaryDiffEq C:\Users\Luffy\.julia\packages\OrdinaryDiffEq\ZgJ9s\src\perform_step\low_order_rk_perform_step.jl:569
 [12] __init(prob::ODEProblem{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, Tuple{Float32, Float32}, false, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, ODEFunction{false, DiffEqFlux.var"#dudt_#133"{NeuralODE{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, GraphNeuralNetworks.var"#re_withgraph#4"{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, Optimisers.Restructure{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}}}}, Tuple{Float32, Float32}, Tuple{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float64, Float64, Bool}}}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, 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::Tuple{}, tstops::Tuple{}, d_discontinuities::Tuple{}, save_idxs::Nothing, save_everystep::Bool, save_on::Bool, save_start::Bool, save_end::Bool, callback::Nothing, 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, Bool, Tuple{Symbol}, NamedTuple{(:save_noise,), Tuple{Bool}}})
    @ OrdinaryDiffEq C:\Users\Luffy\.julia\packages\OrdinaryDiffEq\ZgJ9s\src\solve.jl:456
 [13] #__solve#502
    @ C:\Users\Luffy\.julia\packages\OrdinaryDiffEq\ZgJ9s\src\solve.jl:4 [inlined]
 [14] #solve_call#28
    @ C:\Users\Luffy\.julia\packages\DiffEqBase\hZncn\src\solve.jl:428 [inlined]
 [15] solve_up(prob::ODEProblem{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, Tuple{Float32, Float32}, false, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, ODEFunction{false, DiffEqFlux.var"#dudt_#133"{NeuralODE{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, GraphNeuralNetworks.var"#re_withgraph#4"{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, Optimisers.Restructure{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}}}}, Tuple{Float32, Float32}, Tuple{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float64, Float64, Bool}}}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, sensealg::Nothing, u0::CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, p::CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, args::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}; kwargs::Base.Pairs{Symbol, Real, NTuple{6, Symbol}, NamedTuple{(:save_noise, :save_start, :save_end, :save_everystep, :reltol, :abstol), Tuple{Bool, Bool, Bool, Bool, Float64, Float64}}})
    @ DiffEqBase C:\Users\Luffy\.julia\packages\DiffEqBase\hZncn\src\solve.jl:726
 [16] #solve#29
    @ C:\Users\Luffy\.julia\packages\DiffEqBase\hZncn\src\solve.jl:710 [inlined]
 [17] _concrete_solve_adjoint(::ODEProblem{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, Tuple{Float32, Float32}, false, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, ODEFunction{false, DiffEqFlux.var"#dudt_#133"{NeuralODE{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, GraphNeuralNetworks.var"#re_withgraph#4"{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, Optimisers.Restructure{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}}}}, Tuple{Float32, Float32}, Tuple{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float64, Float64, Bool}}}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), 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}, ::InterpolatingAdjoint{0, true, Val{:central}, ZygoteVJP, Bool}, ::CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, ::CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}; save_start::Bool, save_end::Bool, saveat::Vector{Float32}, save_idxs::Nothing, kwargs::Base.Pairs{Symbol, Real, Tuple{Symbol, Symbol, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol), Tuple{Bool, Float64, Float64}}})
    @ DiffEqSensitivity C:\Users\Luffy\.julia\packages\DiffEqSensitivity\SjURy\src\concrete_solve.jl:131
 [18] #_solve_adjoint#52
    @ C:\Users\Luffy\.julia\packages\DiffEqBase\hZncn\src\solve.jl:1069 [inlined]
 [19] #rrule#50
    @ C:\Users\Luffy\.julia\packages\DiffEqBase\hZncn\src\solve.jl:1032 [inlined]
 [20] chain_rrule_kw
    @ C:\Users\Luffy\.julia\packages\Zygote\DkIUK\src\compiler\chainrules.jl:229 [inlined]
 [21] macro expansion
    @ C:\Users\Luffy\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0 [inlined]
 [22] _pullback
    @ C:\Users\Luffy\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:9 [inlined]
 [23] _apply
    @ .\boot.jl:814 [inlined]
 [24] adjoint
    @ C:\Users\Luffy\.julia\packages\Zygote\DkIUK\src\lib\lib.jl:204 [inlined]
 [25] _pullback
    @ C:\Users\Luffy\.julia\packages\ZygoteRules\AIbCs\src\adjoint.jl:65 [inlined]
 [26] _pullback
    @ C:\Users\Luffy\.julia\packages\DiffEqBase\hZncn\src\solve.jl:710 [inlined]
 [27] _pullback(::Zygote.Context, ::DiffEqBase.var"##solve#29", ::InterpolatingAdjoint{0, true, Val{:central}, ZygoteVJP, Bool}, ::Nothing, ::Nothing, ::Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float64, Float64, Bool}}}, ::typeof(solve), ::ODEProblem{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, Tuple{Float32, Float32}, false, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, ODEFunction{false, DiffEqFlux.var"#dudt_#133"{NeuralODE{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, GraphNeuralNetworks.var"#re_withgraph#4"{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, Optimisers.Restructure{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}}}}, Tuple{Float32, Float32}, Tuple{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float64, Float64, Bool}}}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), 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})
    @ Zygote C:\Users\Luffy\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
 [28] _apply(::Function, ::Vararg{Any})
    @ Core .\boot.jl:814
 [29] adjoint
    @ C:\Users\Luffy\.julia\packages\Zygote\DkIUK\src\lib\lib.jl:204 [inlined]
 [30] _pullback
    @ C:\Users\Luffy\.julia\packages\ZygoteRules\AIbCs\src\adjoint.jl:65 [inlined]
 [31] _pullback
    @ C:\Users\Luffy\.julia\packages\DiffEqBase\hZncn\src\solve.jl:705 [inlined]
 [32] _pullback(::Zygote.Context, ::CommonSolve.var"#solve##kw", ::NamedTuple{(:sensealg, :save_everystep, :reltol, :abstol, :save_start), Tuple{InterpolatingAdjoint{0, true, Val{:central}, ZygoteVJP, Bool}, Bool, Float64, Float64, Bool}}, ::typeof(solve), ::ODEProblem{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, Tuple{Float32, Float32}, false, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, ODEFunction{false, DiffEqFlux.var"#dudt_#133"{NeuralODE{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, GraphNeuralNetworks.var"#re_withgraph#4"{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, Optimisers.Restructure{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}}}}, Tuple{Float32, Float32}, Tuple{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float64, Float64, Bool}}}}}, LinearAlgebra.UniformScaling{Bool}, Nothing, typeof(DiffEqFlux.basic_tgrad), 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})
    @ Zygote C:\Users\Luffy\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
 [33] _apply(::Function, ::Vararg{Any})
    @ Core .\boot.jl:814
 [34] adjoint
    @ C:\Users\Luffy\.julia\packages\Zygote\DkIUK\src\lib\lib.jl:204 [inlined]
 [35] _pullback
    @ C:\Users\Luffy\.julia\packages\ZygoteRules\AIbCs\src\adjoint.jl:65 [inlined]
 [36] _pullback
    @ C:\Users\Luffy\.julia\packages\DiffEqFlux\JicEk\src\neural_de.jl:77 [inlined]
 [37] _pullback(::Zygote.Context, ::NeuralODE{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, GraphNeuralNetworks.var"#re_withgraph#4"{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, Optimisers.Restructure{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}}}}, Tuple{Float32, Float32}, Tuple{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float64, Float64, Bool}}}}, ::CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, ::CuArray{Float32, 1, CUDA.Mem.DeviceBuffer})
    @ Zygote C:\Users\Luffy\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
 [38] _pullback
    @ C:\Users\Luffy\.julia\packages\DiffEqFlux\JicEk\src\neural_de.jl:73 [inlined]
 [39] _pullback(ctx::Zygote.Context, f::NeuralODE{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, GraphNeuralNetworks.var"#re_withgraph#4"{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, Optimisers.Restructure{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}}}}, Tuple{Float32, Float32}, Tuple{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float64, Float64, Bool}}}}, args::CuArray{Float32, 2, CUDA.Mem.DeviceBuffer})
    @ Zygote C:\Users\Luffy\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
 [40] _pullback
    @ C:\Users\Luffy\.julia\packages\GraphNeuralNetworks\4e2gU\src\layers\basic.jl:124 [inlined]
 [41] _pullback(::Zygote.Context, ::typeof(GraphNeuralNetworks.applylayer), ::NeuralODE{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, GraphNeuralNetworks.var"#re_withgraph#4"{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, Optimisers.Restructure{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}}}}, Tuple{Float32, Float32}, Tuple{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float64, Float64, Bool}}}}, ::GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}, ::CuArray{Float32, 2, CUDA.Mem.DeviceBuffer})
    @ Zygote C:\Users\Luffy\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
 [42] _pullback
    @ C:\Users\Luffy\.julia\packages\GraphNeuralNetworks\4e2gU\src\layers\basic.jl:137 [inlined]
--- the last 2 lines are repeated 1 more time ---
 [45] _pullback(::Zygote.Context, ::typeof(GraphNeuralNetworks.applychain), ::Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, NeuralODE{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, GraphNeuralNetworks.var"#re_withgraph#4"{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, Optimisers.Restructure{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}}}}, Tuple{Float32, Float32}, Tuple{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float64, Float64, Bool}}}}, typeof(diffeqsol_to_array), Dense{typeof(identity), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}}, ::GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}, ::CuArray{Float32, 2, CUDA.Mem.DeviceBuffer})
    @ Zygote C:\Users\Luffy\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
 [46] _pullback
    @ C:\Users\Luffy\.julia\packages\GraphNeuralNetworks\4e2gU\src\layers\basic.jl:139 [inlined]
 [47] _pullback(::Zygote.Context, ::GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, NeuralODE{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, GraphNeuralNetworks.var"#re_withgraph#4"{WithGraph{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}}, Optimisers.Restructure{GNNChain{Tuple{GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}, GCNConv{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, typeof(relu)}}}, Tuple{NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}, NamedTuple{(:weight, :bias, :σ, :add_self_loops, :use_edge_weight), Tuple{Int64, Int64, Tuple{}, Tuple{}, Tuple{}}}}}}, Tuple{Float32, Float32}, Tuple{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}, Base.Pairs{Symbol, Real, NTuple{4, Symbol}, NamedTuple{(:save_everystep, :reltol, :abstol, :save_start), Tuple{Bool, Float64, Float64, Bool}}}}, typeof(diffeqsol_to_array), Dense{typeof(identity), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}}}}, ::GNNGraph{Tuple{CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}, Nothing}}, ::CuArray{Float32, 2, CUDA.Mem.DeviceBuffer})
    @ Zygote C:\Users\Luffy\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
 [48] _pullback
    @ .\REPL[37]:3 [inlined]
 [49] _pullback(::Zygote.Context, ::var"#1#2")
    @ Zygote C:\Users\Luffy\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
 [50] pullback(f::Function, ps::Params{Zygote.Buffer{Any, Vector{Any}}})
    @ Zygote C:\Users\Luffy\.julia\packages\Zygote\DkIUK\src\compiler\interface.jl:352
 [51] gradient(f::Function, args::Params{Zygote.Buffer{Any, Vector{Any}}})
    @ Zygote C:\Users\Luffy\.julia\packages\Zygote\DkIUK\src\compiler\interface.jl:75
 [52] top-level scope
    @ REPL[37]:2

If it is working on yours, could you provide me with the loss & accuracy?