fix unbound type parameters in NTuple

Author: ArnoStrouwen

src/layers/mdeq.jl

@@ -94,8 +94,9 @@ function Lux.initialstates(rng::AbstractRNG, deq::MultiScaleDeepEquilibriumNetwo
 end
 
 function MultiScaleDeepEquilibriumNetwork(main_layers::Tuple, mapping_layers::Matrix,
-        post_fuse_layer::Union{Nothing, Tuple}, solver, scales::NTuple{N, NTuple{L, Int64}};
-        sensealg=SteadyStateAdjoint(), kwargs...) where {N, L}
+        post_fuse_layer::Union{Nothing, Tuple}, solver,
+        scales::Tuple{NTuple{L, Int64}, Vararg{NTuple{L, Int64}, nMinus1}};
+        sensealg=SteadyStateAdjoint(), kwargs...) where {nMinus1, L}
     l1 = Parallel(nothing, main_layers...)
     l2 = BranchLayer(Parallel.(+, map(x -> tuple(x...), eachrow(mapping_layers))...)...)
 
@@ -226,8 +227,9 @@ end
 
 function MultiScaleSkipDeepEquilibriumNetwork(main_layers::Tuple, mapping_layers::Matrix,
         post_fuse_layer::Union{Nothing, Tuple}, shortcut_layers::Union{Nothing, Tuple},
-        solver, scales::NTuple{N, NTuple{L, Int64}};
-        sensealg=SteadyStateAdjoint(), kwargs...) where {N, L}
+        solver,
+        scales::Tuple{NTuple{L, Int64}, Vararg{NTuple{L, Int64}, nMinus1}};
+        sensealg=SteadyStateAdjoint(), kwargs...) where {nMinus1, L}
     l1 = Parallel(nothing, main_layers...)
     l2 = BranchLayer(Parallel.(+, map(x -> tuple(x...), eachrow(mapping_layers))...)...)
     shortcut = shortcut_layers === nothing ? nothing : Parallel(nothing, shortcut_layers...)
@@ -330,8 +332,9 @@ model(x, ps, st)
 See also: [`DeepEquilibriumNetwork`](@ref), [`SkipDeepEquilibriumNetwork`](@ref), [`MultiScaleDeepEquilibriumNetwork`](@ref), [`MultiScaleSkipDeepEquilibriumNetwork`](@ref)
 """
 function MultiScaleNeuralODE(main_layers::Tuple, mapping_layers::Matrix,
-        post_fuse_layer::Union{Nothing, Tuple}, solver, scales::NTuple{N, NTuple{L, Int64}};
-        sensealg=GaussAdjoint(; autojacvec=ZygoteVJP()), kwargs...) where {N, L}
+        post_fuse_layer::Union{Nothing, Tuple}, solver,
+        scales::Tuple{NTuple{L, Int64}, Vararg{NTuple{L, Int64}, nMinus1}};
+        sensealg=GaussAdjoint(; autojacvec=ZygoteVJP()), kwargs...) where {nMinus1, L}
     l1 = Parallel(nothing, main_layers...)
     l2 = BranchLayer(Parallel.(+, map(x -> tuple(x...), eachrow(mapping_layers))...)...)
 
@@ -344,7 +347,7 @@ function MultiScaleNeuralODE(main_layers::Tuple, mapping_layers::Matrix,
             split_idxs, scales)
     end
 
-    return MultiScaleNeuralODE{N}(model, solver, sensealg, scales, split_idxs, kwargs)
+    return MultiScaleNeuralODE{nMinus1+1}(model, solver, sensealg, scales, split_idxs, kwargs)
 end
 
 _jacobian_regularization(::MultiScaleNeuralODE) = false

test/qa.jl

@@ -6,6 +6,6 @@ using DeepEquilibriumNetworks, Aqua
     Aqua.test_piracies(DeepEquilibriumNetworks; broken=true)
     Aqua.test_project_extras(DeepEquilibriumNetworks)
     Aqua.test_stale_deps(DeepEquilibriumNetworks)
-    Aqua.test_unbound_args(DeepEquilibriumNetworks; broken=true)
+    Aqua.test_unbound_args(DeepEquilibriumNetworks)
     Aqua.test_undefined_exports(DeepEquilibriumNetworks; broken=true)
 end