Add training examples for mixing Flux and Torch layers

[rejuvyesh]

Currently diffeq examples fails with:

ERROR: LoadError: type Tuple has no field layers
Stacktrace:
  [1] getproperty
    @ ./Base.jl:42 [inlined]
  [2] functor(#unused#::Type{Chain{Tuple{Dense{typeof(tanh), Matrix{Float32}, Vector{Float32}}, TorchModuleWrapper, Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}}}}, c::Tuple{NamedTuple{(:weight, :bias, :σ), Tuple{Int64, Int64, Tuple{}}}, NamedTuple{(:params,), Tuple{NTuple{4, Int64}}}, NamedTuple{(:weight, :bias, :σ), Tuple{Int64, Int64, Tuple{}}}})
    @ Flux ~/.julia/packages/Flux/qAdFM/src/layers/basic.jl:44
  [3] _trainable_biwalk(f::Function, x::Chain{Tuple{Dense{typeof(tanh), Matrix{Float32}, Vector{Float32}}, TorchModuleWrapper, Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}}}, aux::Tuple{NamedTuple{(:weight, :bias, :σ), Tuple{Int64, Int64, Tuple{}}}, NamedTuple{(:params,), Tuple{NTuple{4, Int64}}}, NamedTuple{(:weight, :bias, :σ), Tuple{Int64, Int64, Tuple{}}}})
    @ Optimisers ~/.julia/packages/Optimisers/UAVzc/src/destructure.jl:94
  [4] #fmap#30
    @ ~/.julia/packages/Functors/qBIlC/src/functor.jl:78 [inlined]
  [5] _rebuild(x::Chain{Tuple{Dense{typeof(tanh), Matrix{Float32}, Vector{Float32}}, TorchModuleWrapper, Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}}}, off::Tuple{NamedTuple{(:weight, :bias, :σ), Tuple{Int64, Int64, Tuple{}}}, NamedTuple{(:params,), Tuple{NTuple{4, Int64}}}, NamedTuple{(:weight, :bias, :σ), Tuple{Int64, Int64, Tuple{}}}}, flat::Vector{Float32}, len::Int64; walk::Function, kw::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ Optimisers ~/.julia/packages/Optimisers/UAVzc/src/destructure.jl:83
  [6] _rebuild
    @ ~/.julia/packages/Optimisers/UAVzc/src/destructure.jl:82 [inlined]
  [7] (::Optimisers.Restructure{Chain{Tuple{Dense{typeof(tanh), Matrix{Float32}, Vector{Float32}}, TorchModuleWrapper, Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}}}, Tuple{NamedTuple{(:weight, :bias, :σ), Tuple{Int64, Int64, Tuple{}}}, NamedTuple{(:params,), Tuple{NTuple{4, Int64}}}, NamedTuple{(:weight, :bias, :σ), Tuple{Int64, Int64, Tuple{}}}}})(flat::Vector{Float32})
    @ Optimisers ~/.julia/packages/Optimisers/UAVzc/src/destructure.jl:51
  [8] dudt(u::Vector{Float32}, p::Vector{Float32}, t::Float32)
    @ Main ~/.julia/dev/PyCallChainRules/examples/diffeqflux/simple_mix_node.jl:29
  [9] ODEFunction
    @ ~/.julia/packages/SciMLBase/BoNUy/src/scimlfunctions.jl:345 [inlined]
 [10] initialize!(integrator::OrdinaryDiffEq.ODEIntegrator{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, false, Vector{Float32}, Nothing, Float32, Vector{Float32}, 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}, false, Vector{Float32}, ODEFunction{false, 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{false, 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.Tsit5ConstantCache{Float32, Float32}}, DiffEqBase.DEStats}, ODEFunction{false, typeof(dudt), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing}, OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32}, OrdinaryDiffEq.DEOptions{Float32, Float32, Float32, Float32, PIController{Rational{Int64}}, typeof(DiffEqBase.ODE_DEFAULT_NORM), typeof(LinearAlgebra.opnorm), 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, Tuple{}, StepRangeLen{Float32, Float64, Float64, Int64}, Tuple{}}, Vector{Float32}, Float32, Nothing, OrdinaryDiffEq.DefaultInit}, cache::OrdinaryDiffEq.Tsit5ConstantCache{Float32, Float32})
    @ OrdinaryDiffEq ~/.julia/packages/OrdinaryDiffEq/iN7BJ/src/perform_step/low_order_rk_perform_step.jl:569
 [11] __init(prob::ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, false, Vector{Float32}, ODEFunction{false, 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}, 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::StepRangeLen{Float32, Float64, Float64, Int64}, tstops::Tuple{}, d_discontinuities::Tuple{}, save_idxs::Nothing, save_everystep::Bool, save_on::Bool, save_start::Bool, save_end::Nothing, callback::Nothing, dense::Bool, calck::Bool, dt::Float32, dtmin::Nothing, dtmax::Float32, force_dtmin::Bool, adaptive::Bool, gamma::Rational{Int64}, abstol::Nothing, reltol::Nothing, 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 ~/.julia/packages/OrdinaryDiffEq/iN7BJ/src/solve.jl:456
 [12] #__solve#499
    @ ~/.julia/packages/OrdinaryDiffEq/iN7BJ/src/solve.jl:4 [inlined]
 [13] #solve_call#37
    @ ~/.julia/packages/DiffEqBase/L3EZU/src/solve.jl:61 [inlined]
 [14] solve_up(prob::ODEProblem{Vector{Float32}, Tuple{Float32, Float32}, false, SciMLBase.NullParameters, ODEFunction{false, 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}, sensealg::Nothing, u0::Vector{Float32}, p::Vector{Float32}, args::Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}; kwargs::Base.Pairs{Symbol, StepRangeLen{Float32, Float64, Float64, Int64}, Tuple{Symbol}, NamedTuple{(:saveat,), Tuple{StepRangeLen{Float32, Float64, Float64, Int64}}}})
    @ DiffEqBase ~/.julia/packages/DiffEqBase/L3EZU/src/solve.jl:87
 [15] #solve#38
    @ ~/.julia/packages/DiffEqBase/L3EZU/src/solve.jl:73 [inlined]
 [16] predict_n_ode(p::Vector{Float32})
    @ Main ~/.julia/dev/PyCallChainRules/examples/diffeqflux/simple_mix_node.jl:33
 [17] loss_n_ode(p::Vector{Float32})
    @ Main ~/.julia/dev/PyCallChainRules/examples/diffeqflux/simple_mix_node.jl:37
 [18] top-level scope
    @ ~/.julia/dev/PyCallChainRules/examples/diffeqflux/simple_mix_node.jl:42
 [19] include(fname::String)
    @ Base.MainInclude ./client.jl:451
 [20] top-level scope
    @ REPL[1]:1

Likely an issue with Optimisers.destructure

[rejuvyesh]

Depends on FluxML/Optimisers.jl#62 being fixed. Should already work with Flux master.