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  1. ```
  2. DimensionMismatch("array could not be broadcast to match destination")
  3.  
  4. Stacktrace:
  5. [1] check_broadcast_shape at ./broadcast.jl:520 [inlined]
  6. [2] check_broadcast_axes at ./broadcast.jl:523 [inlined]
  7. [3] instantiate at ./broadcast.jl:269 [inlined]
  8. [4] materialize! at ./broadcast.jl:848 [inlined]
  9. [5] materialize!(::CuArray{Float32,1}, ::Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1},Nothing,typeof(identity),Tuple{CuArray{Float32,1}}}) at ./broadcast.jl:845
  10. [6] _vecjacobian!(::CuArray{Float32,1}, ::CuArray{Float32,4}, ::CuArray{Float32,1}, ::CuArray{Float32,1}, ::Float32, ::DiffEqSensitivity.ODEInterpolatingAdjointSensitivityFunction{DiffEqSensitivity.AdjointDiffCache{Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Base.OneTo{Int64},UnitRange{Int64},LinearAlgebra.UniformScaling{Bool}},DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},CuArray{Float32,4},ODESolution{Float32,5,Array{CuArray{Float32,4},1},Nothing,Nothing,Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},ODEProblem{CuArray{Float32,4},Tuple{Float32,Float32},false,CuArray{Float32,1},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Array{CuArray{Float32,4},1},Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float32,Float32}},DiffEqBase.DEStats},DiffEqSensitivity.CheckpointSolution{ODESolution{Float32,5,Array{CuArray{Float32,4},1},Nothing,Nothing,Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},ODEProblem{CuArray{Float32,4},Tuple{Float32,Float32},false,CuArray{Float32,1},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Array{CuArray{Float32,4},1},Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float32,Float32}},DiffEqBase.DEStats},Array{Tuple{Float32,Float32},1},NamedTuple{(:reltol, :abstol),Tuple{Float64,Float64}}},ODEProblem{CuArray{Float32,4},Tuple{Float32,Float32},false,CuArray{Float32,1},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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}}, ::DiffEqSensitivity.ZygoteVJP, ::CuArray{Float32,1}, ::Nothing) at /home/swamy/.julia/packages/DiffEqSensitivity/WiCRA/src/local_sensitivity/derivative_wrappers.jl:296
  11. [7] #vecjacobian!#20 at /home/swamy/.julia/packages/DiffEqSensitivity/WiCRA/src/local_sensitivity/derivative_wrappers.jl:147 [inlined]
  12. [8] (::DiffEqSensitivity.ODEInterpolatingAdjointSensitivityFunction{DiffEqSensitivity.AdjointDiffCache{Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Base.OneTo{Int64},UnitRange{Int64},LinearAlgebra.UniformScaling{Bool}},DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},CuArray{Float32,4},ODESolution{Float32,5,Array{CuArray{Float32,4},1},Nothing,Nothing,Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},ODEProblem{CuArray{Float32,4},Tuple{Float32,Float32},false,CuArray{Float32,1},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Array{CuArray{Float32,4},1},Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float32,Float32}},DiffEqBase.DEStats},DiffEqSensitivity.CheckpointSolution{ODESolution{Float32,5,Array{CuArray{Float32,4},1},Nothing,Nothing,Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},ODEProblem{CuArray{Float32,4},Tuple{Float32,Float32},false,CuArray{Float32,1},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Array{CuArray{Float32,4},1},Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float32,Float32}},DiffEqBase.DEStats},Array{Tuple{Float32,Float32},1},NamedTuple{(:reltol, :abstol),Tuple{Float64,Float64}}},ODEProblem{CuArray{Float32,4},Tuple{Float32,Float32},false,CuArray{Float32,1},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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}})(::CuArray{Float32,1}, ::CuArray{Float32,1}, ::CuArray{Float32,1}, ::Float32) at /home/swamy/.julia/packages/DiffEqSensitivity/WiCRA/src/local_sensitivity/interpolating_adjoint.jl:145
  13. [9] (::ODEFunction{true,DiffEqSensitivity.ODEInterpolatingAdjointSensitivityFunction{DiffEqSensitivity.AdjointDiffCache{Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Base.OneTo{Int64},UnitRange{Int64},LinearAlgebra.UniformScaling{Bool}},DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},CuArray{Float32,4},ODESolution{Float32,5,Array{CuArray{Float32,4},1},Nothing,Nothing,Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},ODEProblem{CuArray{Float32,4},Tuple{Float32,Float32},false,CuArray{Float32,1},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Array{CuArray{Float32,4},1},Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float32,Float32}},DiffEqBase.DEStats},DiffEqSensitivity.CheckpointSolution{ODESolution{Float32,5,Array{CuArray{Float32,4},1},Nothing,Nothing,Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},ODEProblem{CuArray{Float32,4},Tuple{Float32,Float32},false,CuArray{Float32,1},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.Pairs{Symbol,Real,NTuple{4,Symbol},NamedTuple{(:save_everystep, :reltol, :abstol, 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  14. [10] 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::OrdinaryDiffEq.Tsit5Cache{CuArray{Float32,1},CuArray{Float32,1},CuArray{Float32,1},OrdinaryDiffEq.Tsit5ConstantCache{Float32,Float32}}) at /home/swamy/.julia/packages/OrdinaryDiffEq/OK16j/src/perform_step/low_order_rk_perform_step.jl:623
  15. [11] __init(::ODEProblem{CuArray{Float32,1},Tuple{Float32,Float32},true,CuArray{Float32,1},ODEFunction{true,DiffEqSensitivity.ODEInterpolatingAdjointSensitivityFunction{DiffEqSensitivity.AdjointDiffCache{Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Base.OneTo{Int64},UnitRange{Int64},LinearAlgebra.UniformScaling{Bool}},DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},CuArray{Float32,4},ODESolution{Float32,5,Array{CuArray{Float32,4},1},Nothing,Nothing,Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},ODEProblem{CuArray{Float32,4},Tuple{Float32,Float32},false,CuArray{Float32,1},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.Pairs{Symbol,Real,NTuple{4,Symbol},NamedTuple{(:save_everystep, 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:abstol),Tuple{Float64,Float64}}},ODEProblem{CuArray{Float32,4},Tuple{Float32,Float32},false,CuArray{Float32,1},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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}},LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Symbol,CallbackSet{Tuple{},Tuple{DiscreteCallback{DiffEqCallbacks.var"#33#38"{Base.RefValue{Union{Nothing, Float32}}},DiffEqCallbacks.var"#34#39"{DiffEqSensitivity.var"#94#96"{Base.RefValue{Int64},Array{Float32,1}},DiffEqSensitivity.var"#95#97"{DiffEqSensitivity.var"#df#134"{CuArray{Float32,5},CuArray{Float32,4},Colon},DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},Base.OneTo{Int64},UnitRange{Int64},LinearAlgebra.UniformScaling{Bool},Bool,Nothing,Nothing,Nothing,Nothing,Bool,CuArray{Float32,1},Array{Float32,1},CuArray{Float32,4},Base.RefValue{Int64},Int64,LinearAlgebra.UniformScaling{Bool}},Base.RefValue{Union{Nothing, Float32}}},DiffEqCallbacks.var"#35#40"{Bool,DiffEqCallbacks.var"#37#42"{Bool},DiffEqSensitivity.var"#94#96"{Base.RefValue{Int64},Array{Float32,1}},Base.RefValue{Union{Nothing, Float32}},DiffEqCallbacks.var"#34#39"{DiffEqSensitivity.var"#94#96"{Base.RefValue{Int64},Array{Float32,1}},DiffEqSensitivity.var"#95#97"{DiffEqSensitivity.var"#df#134"{CuArray{Float32,5},CuArray{Float32,4},Colon},DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},Base.OneTo{Int64},UnitRange{Int64},LinearAlgebra.UniformScaling{Bool},Bool,Nothing,Nothing,Nothing,Nothing,Bool,CuArray{Float32,1},Array{Float32,1},CuArray{Float32,4},Base.RefValue{Int64},Int64,LinearAlgebra.UniformScaling{Bool}},Base.RefValue{Union{Nothing, Float32}}}}}}},Tuple{Symbol},NamedTuple{(:callback,),Tuple{CallbackSet{Tuple{},Tuple{DiscreteCallback{DiffEqCallbacks.var"#33#38"{Base.RefValue{Union{Nothing, Float32}}},DiffEqCallbacks.var"#34#39"{DiffEqSensitivity.var"#94#96"{Base.RefValue{Int64},Array{Float32,1}},DiffEqSensitivity.var"#95#97"{DiffEqSensitivity.var"#df#134"{CuArray{Float32,5},CuArray{Float32,4},Colon},DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},Base.OneTo{Int64},UnitRange{Int64},LinearAlgebra.UniformScaling{Bool},Bool,Nothing,Nothing,Nothing,Nothing,Bool,CuArray{Float32,1},Array{Float32,1},CuArray{Float32,4},Base.RefValue{Int64},Int64,LinearAlgebra.UniformScaling{Bool}},Base.RefValue{Union{Nothing, Float32}}},DiffEqCallbacks.var"#35#40"{Bool,DiffEqCallbacks.var"#37#42"{Bool},DiffEqSensitivity.var"#94#96"{Base.RefValue{Int64},Array{Float32,1}},Base.RefValue{Union{Nothing, Float32}},DiffEqCallbacks.var"#34#39"{DiffEqSensitivity.var"#94#96"{Base.RefValue{Int64},Array{Float32,1}},DiffEqSensitivity.var"#95#97"{DiffEqSensitivity.var"#df#134"{CuArray{Float32,5},CuArray{Float32,4},Colon},DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},Base.OneTo{Int64},UnitRange{Int64},LinearAlgebra.UniformScaling{Bool},Bool,Nothing,Nothing,Nothing,Nothing,Bool,CuArray{Float32,1},Array{Float32,1},CuArray{Float32,4},Base.RefValue{Int64},Int64,LinearAlgebra.UniformScaling{Bool}},Base.RefValue{Union{Nothing, Float32}}}}}}}}}},DiffEqBase.StandardODEProblem}, ::Tsit5, ::Tuple{}, ::Tuple{}, ::Tuple{}, ::Type{Val{true}}; saveat::Array{Float32,1}, tstops::Array{Float32,1}, d_discontinuities::Tuple{}, save_idxs::Nothing, save_everystep::Bool, save_on::Bool, save_start::Bool, save_end::Bool, callback::CallbackSet{Tuple{},Tuple{DiscreteCallback{DiffEqCallbacks.var"#33#38"{Base.RefValue{Union{Nothing, Float32}}},DiffEqCallbacks.var"#34#39"{DiffEqSensitivity.var"#94#96"{Base.RefValue{Int64},Array{Float32,1}},DiffEqSensitivity.var"#95#97"{DiffEqSensitivity.var"#df#134"{CuArray{Float32,5},CuArray{Float32,4},Colon},DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},Base.OneTo{Int64},UnitRange{Int64},LinearAlgebra.UniformScaling{Bool},Bool,Nothing,Nothing,Nothing,Nothing,Bool,CuArray{Float32,1},Array{Float32,1},CuArray{Float32,4},Base.RefValue{Int64},Int64,LinearAlgebra.UniformScaling{Bool}},Base.RefValue{Union{Nothing, Float32}}},DiffEqCallbacks.var"#35#40"{Bool,DiffEqCallbacks.var"#37#42"{Bool},DiffEqSensitivity.var"#94#96"{Base.RefValue{Int64},Array{Float32,1}},Base.RefValue{Union{Nothing, Float32}},DiffEqCallbacks.var"#34#39"{DiffEqSensitivity.var"#94#96"{Base.RefValue{Int64},Array{Float32,1}},DiffEqSensitivity.var"#95#97"{DiffEqSensitivity.var"#df#134"{CuArray{Float32,5},CuArray{Float32,4},Colon},DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},Base.OneTo{Int64},UnitRange{Int64},LinearAlgebra.UniformScaling{Bool},Bool,Nothing,Nothing,Nothing,Nothing,Bool,CuArray{Float32,1},Array{Float32,1},CuArray{Float32,4},Base.RefValue{Int64},Int64,LinearAlgebra.UniformScaling{Bool}},Base.RefValue{Union{Nothing, Float32}}}}}}}, 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, qoldinit::Rational{Int64}, fullnormalize::Bool, failfactor::Int64, beta1::Nothing, beta2::Nothing, 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.Iterators.Pairs{Symbol,DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},Tuple{Symbol},NamedTuple{(:sense,),Tuple{DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool}}}}) at /home/swamy/.julia/packages/OrdinaryDiffEq/OK16j/src/solve.jl:429
  16. [12] #__solve#398 at /home/swamy/.julia/packages/OrdinaryDiffEq/OK16j/src/solve.jl:4 [inlined]
  17. [13] solve_call(::ODEProblem{CuArray{Float32,1},Tuple{Float32,Float32},true,CuArray{Float32,1},ODEFunction{true,DiffEqSensitivity.ODEInterpolatingAdjointSensitivityFunction{DiffEqSensitivity.AdjointDiffCache{Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Base.OneTo{Int64},UnitRange{Int64},LinearAlgebra.UniformScaling{Bool}},DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},CuArray{Float32,4},ODESolution{Float32,5,Array{CuArray{Float32,4},1},Nothing,Nothing,Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},ODEProblem{CuArray{Float32,4},Tuple{Float32,Float32},false,CuArray{Float32,1},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Array{CuArray{Float32,4},1},Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float32,Float32}},DiffEqBase.DEStats},DiffEqSensitivity.CheckpointSolution{ODESolution{Float32,5,Array{CuArray{Float32,4},1},Nothing,Nothing,Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},ODEProblem{CuArray{Float32,4},Tuple{Float32,Float32},false,CuArray{Float32,1},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Array{CuArray{Float32,4},1},Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float32,Float32}},DiffEqBase.DEStats},Array{Tuple{Float32,Float32},1},NamedTuple{(:reltol, :abstol),Tuple{Float64,Float64}}},ODEProblem{CuArray{Float32,4},Tuple{Float32,Float32},false,CuArray{Float32,1},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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}},LinearAlgebra.UniformScaling{Bool},Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing,Nothing},Base.Iterators.Pairs{Symbol,CallbackSet{Tuple{},Tuple{DiscreteCallback{DiffEqCallbacks.var"#33#38"{Base.RefValue{Union{Nothing, Float32}}},DiffEqCallbacks.var"#34#39"{DiffEqSensitivity.var"#94#96"{Base.RefValue{Int64},Array{Float32,1}},DiffEqSensitivity.var"#95#97"{DiffEqSensitivity.var"#df#134"{CuArray{Float32,5},CuArray{Float32,4},Colon},DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},Base.OneTo{Int64},UnitRange{Int64},LinearAlgebra.UniformScaling{Bool},Bool,Nothing,Nothing,Nothing,Nothing,Bool,CuArray{Float32,1},Array{Float32,1},CuArray{Float32,4},Base.RefValue{Int64},Int64,LinearAlgebra.UniformScaling{Bool}},Base.RefValue{Union{Nothing, Float32}}},DiffEqCallbacks.var"#35#40"{Bool,DiffEqCallbacks.var"#37#42"{Bool},DiffEqSensitivity.var"#94#96"{Base.RefValue{Int64},Array{Float32,1}},Base.RefValue{Union{Nothing, Float32}},DiffEqCallbacks.var"#34#39"{DiffEqSensitivity.var"#94#96"{Base.RefValue{Int64},Array{Float32,1}},DiffEqSensitivity.var"#95#97"{DiffEqSensitivity.var"#df#134"{CuArray{Float32,5},CuArray{Float32,4},Colon},DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},Base.OneTo{Int64},UnitRange{Int64},LinearAlgebra.UniformScaling{Bool},Bool,Nothing,Nothing,Nothing,Nothing,Bool,CuArray{Float32,1},Array{Float32,1},CuArray{Float32,4},Base.RefValue{Int64},Int64,LinearAlgebra.UniformScaling{Bool}},Base.RefValue{Union{Nothing, Float32}}}}}}},Tuple{Symbol},NamedTuple{(:callback,),Tuple{CallbackSet{Tuple{},Tuple{DiscreteCallback{DiffEqCallbacks.var"#33#38"{Base.RefValue{Union{Nothing, Float32}}},DiffEqCallbacks.var"#34#39"{DiffEqSensitivity.var"#94#96"{Base.RefValue{Int64},Array{Float32,1}},DiffEqSensitivity.var"#95#97"{DiffEqSensitivity.var"#df#134"{CuArray{Float32,5},CuArray{Float32,4},Colon},DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},Base.OneTo{Int64},UnitRange{Int64},LinearAlgebra.UniformScaling{Bool},Bool,Nothing,Nothing,Nothing,Nothing,Bool,CuArray{Float32,1},Array{Float32,1},CuArray{Float32,4},Base.RefValue{Int64},Int64,LinearAlgebra.UniformScaling{Bool}},Base.RefValue{Union{Nothing, Float32}}},DiffEqCallbacks.var"#35#40"{Bool,DiffEqCallbacks.var"#37#42"{Bool},DiffEqSensitivity.var"#94#96"{Base.RefValue{Int64},Array{Float32,1}},Base.RefValue{Union{Nothing, Float32}},DiffEqCallbacks.var"#34#39"{DiffEqSensitivity.var"#94#96"{Base.RefValue{Int64},Array{Float32,1}},DiffEqSensitivity.var"#95#97"{DiffEqSensitivity.var"#df#134"{CuArray{Float32,5},CuArray{Float32,4},Colon},DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},Base.OneTo{Int64},UnitRange{Int64},LinearAlgebra.UniformScaling{Bool},Bool,Nothing,Nothing,Nothing,Nothing,Bool,CuArray{Float32,1},Array{Float32,1},CuArray{Float32,4},Base.RefValue{Int64},Int64,LinearAlgebra.UniformScaling{Bool}},Base.RefValue{Union{Nothing, Float32}}}}}}}}}},DiffEqBase.StandardODEProblem}, ::Tsit5; merge_callbacks::Bool, kwargs::Base.Iterators.Pairs{Symbol,Any,NTuple{7,Symbol},NamedTuple{(:save_everystep, :save_start, :saveat, :tstops, :abstol, :reltol, :sense),Tuple{Bool,Bool,Array{Float32,1},Array{Float32,1},Float64,Float64,DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool}}}}) at /home/swamy/.julia/packages/DiffEqBase/3iigH/src/solve.jl:65
  18. [14] #solve_up#458 at /home/swamy/.julia/packages/DiffEqBase/3iigH/src/solve.jl:86 [inlined]
  19. [15] #solve#457 at /home/swamy/.julia/packages/DiffEqBase/3iigH/src/solve.jl:74 [inlined]
  20. [16] _adjoint_sensitivities(::ODESolution{Float32,5,Array{CuArray{Float32,4},1},Nothing,Nothing,Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},ODEProblem{CuArray{Float32,4},Tuple{Float32,Float32},false,CuArray{Float32,1},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Array{CuArray{Float32,4},1},Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float32,Float32}},DiffEqBase.DEStats}, ::DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool}, ::Tsit5, ::DiffEqSensitivity.var"#df#134"{CuArray{Float32,5},CuArray{Float32,4},Colon}, ::Array{Float32,1}, ::Nothing; abstol::Float64, reltol::Float64, checkpoints::Array{Float32,1}, kwargs::Base.Iterators.Pairs{Symbol,Any,Tuple{Symbol,Symbol},NamedTuple{(:sense, :save_everystep),Tuple{DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},Bool}}}) at /home/swamy/.julia/packages/DiffEqSensitivity/WiCRA/src/local_sensitivity/sensitivity_interface.jl:22
  21. [17] adjoint_sensitivities(::ODESolution{Float32,5,Array{CuArray{Float32,4},1},Nothing,Nothing,Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},ODEProblem{CuArray{Float32,4},Tuple{Float32,Float32},false,CuArray{Float32,1},ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},DiffEqBase.StandardODEProblem},Tsit5,OrdinaryDiffEq.InterpolationData{ODEFunction{false,DiffEqFlux.var"#dudt_#144"{NeuralODE{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}},CuArray{Float32,1},Flux.var"#34#36"{Chain{Tuple{Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32},Conv{2,4,typeof(relu),CuArray{Float32,4},CuArray{Float32,1}},BatchNorm{typeof(identity),CuArray{Float32,1},CuArray{Float32,1},Float32}}}},Tuple{Float32,Float32},Tuple{Tsit5},Base.Iterators.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},Array{CuArray{Float32,4},1},Array{Float32,1},Array{Array{CuArray{Float32,4},1},1},OrdinaryDiffEq.Tsit5ConstantCache{Float32,Float32}},DiffEqBase.DEStats}, ::Tsit5, ::Vararg{Any,N} where N; sensealg::DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool}, kwargs::Base.Iterators.Pairs{Symbol,Any,NTuple{4,Symbol},NamedTuple{(:sense, :save_everystep, :reltol, :abstol),Tuple{DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},Bool,Float64,Float64}}}) at /home/swamy/.julia/packages/DiffEqSensitivity/WiCRA/src/local_sensitivity/sensitivity_interface.jl:6
  22. [18] (::DiffEqSensitivity.var"#adjoint_sensitivity_backpass#133"{Tsit5,DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},CuArray{Float32,4},CuArray{Float32,1},Tuple{},Colon})(::CuArray{Float32,5}) at /home/swamy/.julia/packages/DiffEqSensitivity/WiCRA/src/local_sensitivity/concrete_solve.jl:144
  23. [19] #673#back at /home/swamy/.julia/packages/ZygoteRules/OjfTt/src/adjoint.jl:65 [inlined]
  24. [20] #150 at /home/swamy/.julia/packages/Zygote/ggM8Z/src/lib/lib.jl:191 [inlined]
  25. [21] (::Zygote.var"#1693#back#152"{Zygote.var"#150#151"{DiffEqBase.var"#673#back#471"{DiffEqSensitivity.var"#adjoint_sensitivity_backpass#133"{Tsit5,DiffEqSensitivity.InterpolatingAdjoint{0,true,Val{:central},DiffEqSensitivity.ZygoteVJP,Bool},CuArray{Float32,4},CuArray{Float32,1},Tuple{},Colon}},Tuple{NTuple{6,Nothing},Tuple{Nothing}}}})(::CuArray{Float32,5}) at /home/swamy/.julia/packages/ZygoteRules/OjfTt/src/adjoint.jl:59
  26. [22] #solve#457 at /home/swamy/.julia/packages/DiffEqBase/3iigH/src/solve.jl:74 [inlined]
  27. [23] (::typeof(∂(#solve#457)))(::CuArray{Float32,5}) at /home/swamy/.julia/packages/Zygote/ggM8Z/src/compiler/interface2.jl:0
  28. [24] (::Zygote.var"#150#151"{typeof(∂(#solve#457)),Tuple{NTuple{6,Nothing},Tuple{Nothing}}})(::CuArray{Float32,5}) at /home/swamy/.julia/packages/Zygote/ggM8Z/src/lib/lib.jl:191
  29. [25] (::Zygote.var"#1693#back#152"{Zygote.var"#150#151"{typeof(∂(#solve#457)),Tuple{NTuple{6,Nothing},Tuple{Nothing}}}})(::CuArray{Float32,5}) at /home/swamy/.julia/packages/ZygoteRules/OjfTt/src/adjoint.jl:59
  30. [26] (::typeof(∂(solve##kw)))(::CuArray{Float32,5}) at /home/swamy/.julia/packages/Zygote/ggM8Z/src/compiler/interface2.jl:0
  31. [27] (::Zygote.var"#150#151"{typeof(∂(solve##kw)),Tuple{Tuple{Nothing,Nothing,Nothing},Tuple{Nothing}}})(::CuArray{Float32,5}) at /home/swamy/.julia/packages/Zygote/ggM8Z/src/lib/lib.jl:191
  32. [28] #1693#back at /home/swamy/.julia/packages/ZygoteRules/OjfTt/src/adjoint.jl:59 [inlined]
  33. [29] NeuralODE at /home/swamy/.julia/packages/DiffEqFlux/wifQ9/src/neural_de.jl:69 [inlined]
  34. [30] (::typeof(∂(λ)))(::CuArray{Float32,5}) at /home/swamy/.julia/packages/Zygote/ggM8Z/src/compiler/interface2.jl:0
  35. [31] NeuralODE at /home/swamy/.julia/packages/DiffEqFlux/wifQ9/src/neural_de.jl:65 [inlined]
  36. [32] (::typeof(∂(λ)))(::CuArray{Float32,5}) at /home/swamy/.julia/packages/Zygote/ggM8Z/src/compiler/interface2.jl:0
  37. [33] applychain at /home/swamy/.julia/packages/Flux/05b38/src/layers/basic.jl:36 [inlined]
  38. [34] (::typeof(∂(applychain)))(::CuArray{Float32,2}) at /home/swamy/.julia/packages/Zygote/ggM8Z/src/compiler/interface2.jl:0
  39. [35] applychain at /home/swamy/.julia/packages/Flux/05b38/src/layers/basic.jl:36 [inlined]
  40. [36] (::typeof(∂(applychain)))(::CuArray{Float32,2}) at /home/swamy/.julia/packages/Zygote/ggM8Z/src/compiler/interface2.jl:0
  41. [37] Chain at /home/swamy/.julia/packages/Flux/05b38/src/layers/basic.jl:38 [inlined]
  42. [38] (::typeof(∂(λ)))(::CuArray{Float32,2}) at /home/swamy/.julia/packages/Zygote/ggM8Z/src/compiler/interface2.jl:0
  43. [39] loss at ./In[47]:1 [inlined]
  44. [40] (::typeof(∂(loss)))(::Float32) at /home/swamy/.julia/packages/Zygote/ggM8Z/src/compiler/interface2.jl:0
  45. [41] #43 at ./In[50]:1 [inlined]
  46. [42] (::typeof(∂(#43)))(::Float32) at /home/swamy/.julia/packages/Zygote/ggM8Z/src/compiler/interface2.jl:0
  47. [43] (::Zygote.var"#54#55"{Zygote.Params,Zygote.Context,typeof(∂(#43))})(::Float32) at /home/swamy/.julia/packages/Zygote/ggM8Z/src/compiler/interface.jl:172
  48. [44] gradient(::Function, ::Zygote.Params) at /home/swamy/.julia/packages/Zygote/ggM8Z/src/compiler/interface.jl:49
  49. [45] top-level scope at In[50]:1
  50. [46] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091
  51.  
  52. ```
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