diff --git a/GNNLux/test/layers/temp.jl b/GNNLux/test/layers/temp.jl
deleted file mode 100644
index 46e4ba1a2..000000000
--- a/GNNLux/test/layers/temp.jl
+++ /dev/null
@@ -1,94 +0,0 @@
-
-
-@testset "GINConv" begin
-    nn = Chain(Dense(in_dims => out_dims, relu), Dense(out_dims => out_dims))
-    l = GINConv(nn, 0.5)
-    test_lux_layer(rng, l, g, x, sizey=(out_dims,g.num_nodes), container=true)
-end
-
-@testset "SGConv" begin
-    l = SGConv(in_dims => out_dims, 2)
-    test_lux_layer(rng, l, g, x, outputsize=(out_dims,))
-end
-
-
-
-function test_lux_layer(rng::AbstractRNG, l, g::GNNGraph, x; 
-            outputsize=nothing, sizey=nothing, container=false,
-            atol=1.0f-2, rtol=1.0f-2)
-
-
-    ps = LuxCore.initialparameters(rng, l)
-    st = LuxCore.initialstates(rng, l)
-    @test LuxCore.parameterlength(l) == LuxCore.parameterlength(ps)
-    @test LuxCore.statelength(l) == LuxCore.statelength(st)
-    
-    y, st′ = l(g, x, ps, st)
-    @test eltype(y) == eltype(x)
-    if outputsize !== nothing
-        @test LuxCore.outputsize(l) == outputsize
-    end
-    if sizey !== nothing
-        @test size(y) == sizey
-    elseif outputsize !== nothing
-        @test size(y) == (outputsize..., g.num_nodes)
-    end
-    
-    loss = (x, ps) -> sum(first(l(g, x, ps, st)))
-    test_gradients(loss, x, ps; atol, rtol, skip_backends=[AutoReverseDiff(), AutoTracker(), AutoForwardDiff(), AutoEnzyme()])
-end
-
-using LuxTestUtils: test_gradients, AutoReverseDiff, AutoTracker, AutoForwardDiff, AutoEnzyme
-using StableRNGs
-
-"""
-MEGNetConv{Flux.Chain{Tuple{Flux.Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}}}, Flux.Chain{Tuple{Flux.Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Flux.Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}}}, typeof(mean)}(Chain(Dense(9 => 5, relu), Dense(5 => 5)), Chain(Dense(8 => 5, relu), Dense(5 => 5)), Statistics.mean)
-"""
-
-g = rand_graph(10, 40, seed=1234)
-    in_dims = 3
-    out_dims = 5
-    x = randn(Float32, in_dims, 10)
-    rng = StableRNG(1234)
-    l = MEGNetConv(in_dims => out_dims)
-    l
-    l isa GNNContainerLayer
-    test_lux_layer(rng, l, g, x, sizey=((out_dims, g.num_nodes), (out_dims, g.num_edges)), container=true)
-
-
-        ps = LuxCore.initialparameters(rng, l)
-        st = LuxCore.initialstates(rng, l)
-        edata = rand(T, in_channel, g.num_edges)
-
-        (x_new, e_new), st_new = l(g, x, ps, st)
-        
-        @test size(x_new) == (out_dims, g.num_nodes)
-        @test size(e_new) == (out_dims, g.num_edges)
-        
-
-nn = Chain(Dense(in_dims => out_dims, relu), Dense(out_dims => out_dims))
-l = GINConv(nn, 0.5)
-test_lux_layer(rng, l, g, x, sizey=(out_dims,g.num_nodes), container=true)
-
-
-
-    hin = 6
-    hout = 7
-    hidden = 8
-    l = EGNNConv(hin => hout, hidden)
-    ps = LuxCore.initialparameters(rng, l)
-    st = LuxCore.initialstates(rng, l)
-    h = randn(rng, Float32, hin, g.num_nodes)
-    (hnew, xnew), stnew = l(g, h, x, ps, st)
-    @test size(hnew) == (hout, g.num_nodes)
-    @test size(xnew) == (in_dims, g.num_nodes)
-
-
-    l = MEGNetConv(in_dims => out_dims)
-    l
-    l isa GNNContainerLayer
-    test_lux_layer(rng, l, g, x, sizey=((out_dims, g.num_nodes), (out_dims, g.num_edges)), container=true)
-
-
-        ps = LuxCore.initialparameters(rng, l)
-        st = LuxCore.initialstates(rng, l)
\ No newline at end of file