doctest failure in ~/work/SymbolicNeuralNetworks.jl/SymbolicNeuralNetworks.jl/src/derivatives/pullback.jl:42-68 ```jldoctest using SymbolicNeuralNetworks using AbstractNeuralNetworks using Symbolics import Random Random.seed!(123) c = Chain(Dense(2, 1, tanh)) nn = NeuralNetwork(c) snn = SymbolicNeuralNetwork(nn) loss = FeedForwardLoss() pb = SymbolicPullback(snn, loss) input_output = (rand(2), rand(1)) loss_and_pullback = pb(nn.params, nn.model, input_output) # note that we apply the second argument to another input `1` pb_values = loss_and_pullback[2](1) @variables soutput[1:SymbolicNeuralNetworks.output_dimension(nn.model)] symbolic_pullbacks = SymbolicNeuralNetworks.symbolic_pullback(loss(nn.model, nn.params, nn.input, soutput), nn) pb_values2 = build_nn_function(symbolic_pullbacks, nn.params, nn.input, soutput)(input_output[1], input_output[2], ps) pb_values == (pb_values2 |> SymbolicNeuralNetworks._get_params |> SymbolicNeuralNetworks._get_contents) # output true ``` Subexpression: using SymbolicNeuralNetworks using AbstractNeuralNetworks using Symbolics import Random Random.seed!(123) c = Chain(Dense(2, 1, tanh)) nn = NeuralNetwork(c) snn = SymbolicNeuralNetwork(nn) loss = FeedForwardLoss() pb = SymbolicPullback(snn, loss) input_output = (rand(2), rand(1)) loss_and_pullback = pb(nn.params, nn.model, input_output) # note that we apply the second argument to another input `1` pb_values = loss_and_pullback[2](1) @variables soutput[1:SymbolicNeuralNetworks.output_dimension(nn.model)] symbolic_pullbacks = SymbolicNeuralNetworks.symbolic_pullback(loss(nn.model, nn.params, nn.input, soutput), nn) pb_values2 = build_nn_function(symbolic_pullbacks, nn.params, nn.input, soutput)(input_output[1], input_output[2], ps) pb_values == (pb_values2 |> SymbolicNeuralNetworks._get_params |> SymbolicNeuralNetworks._get_contents) Evaluated output: ERROR: type NeuralNetwork has no field input Stacktrace: [1] getproperty(x::NeuralNetwork{AbstractNeuralNetworks.UnknownArchitecture, Chain{Tuple{Dense{2, 1, true, GenericActivation{typeof(tanh)}}}}, NeuralNetworkParameters{(:L1,), Tuple{@NamedTuple{W::Matrix{Float64}, b::Vector{Float64}}}}, CPU}, f::Symbol) @ Base ./Base.jl:49 [2] top-level scope @ none:1 Expected output: true diff = Warning: Diff output requires color. trueERROR: type NeuralNetwork has no field input Stacktrace: [1] getproperty(x::NeuralNetwork{AbstractNeuralNetworks.UnknownArchitecture, Chain{Tuple{Dense{2, 1, true, GenericActivation{typeof(tanh)}}}}, NeuralNetworkParameters{(:L1,), Tuple{@NamedTuple{W::Matrix{Float64}, b::Vector{Float64}}}}, CPU}, f::Symbol) @ Base ./Base.jl:49 [2] top-level scope @ none:1