diff --git a/GNNLux/test/layers/basic_tests.jl b/GNNLux/test/layers/basic_tests.jl
index 11a1d3a29..9f59f3b10 100644
--- a/GNNLux/test/layers/basic_tests.jl
+++ b/GNNLux/test/layers/basic_tests.jl
@@ -7,18 +7,13 @@
         @test GNNLayer <: LuxCore.AbstractExplicitLayer
     end
 
+    @testset "GNNContainerLayer" begin
+        @test GNNContainerLayer <: LuxCore.AbstractExplicitContainerLayer
+    end
+
     @testset "GNNChain" begin
         @test GNNChain <: LuxCore.AbstractExplicitContainerLayer{(:layers,)}
-        @test GNNChain <: GNNContainerLayer
         c = GNNChain(GraphConv(3 => 5, relu), GCNConv(5 => 3))
-        ps = LuxCore.initialparameters(rng, c)
-        st = LuxCore.initialstates(rng, c)
-        @test LuxCore.parameterlength(c) == LuxCore.parameterlength(ps)
-        @test LuxCore.statelength(c) == LuxCore.statelength(st)
-        y, st′ = c(g, x, ps, st)
-        @test LuxCore.outputsize(c) == (3,)
-        @test size(y) == (3, 10)
-        loss = (x, ps) -> sum(first(c(g, x, ps, st)))
-        @eval @test_gradients $loss $x $ps atol=1.0f-3 rtol=1.0f-3 skip_tracker=true skip_reverse_diff=true
+        test_lux_layer(rng, c, g, x, outputsize=(3,), container=true)
     end
 end
diff --git a/GNNLux/test/layers/conv_tests.jl b/GNNLux/test/layers/conv_tests.jl
index ca36bcbb0..520fcc570 100644
--- a/GNNLux/test/layers/conv_tests.jl
+++ b/GNNLux/test/layers/conv_tests.jl
@@ -5,89 +5,32 @@
 
     @testset "GCNConv" begin
         l = GCNConv(3 => 5, relu)
-        @test l isa GNNLayer
-        ps = Lux.initialparameters(rng, l)
-        st = Lux.initialstates(rng, l)
-        @test Lux.parameterlength(l) == Lux.parameterlength(ps)
-        @test Lux.statelength(l) == Lux.statelength(st)
-
-        y, _ = l(g, x, ps, st)
-        @test Lux.outputsize(l) == (5,)
-        @test size(y) == (5, 10)
-        loss = (x, ps) -> sum(first(l(g, x, ps, st)))
-        @eval @test_gradients $loss $x $ps atol=1.0f-3 rtol=1.0f-3 skip_tracker=true
+        test_lux_layer(rng, l, g, x, outputsize=(5,))
     end
 
     @testset "ChebConv" begin
         l = ChebConv(3 => 5, 2)
-        @test l isa GNNLayer
-        ps = Lux.initialparameters(rng, l)
-        st = Lux.initialstates(rng, l)
-        @test Lux.parameterlength(l) == Lux.parameterlength(ps)
-        @test Lux.statelength(l) == Lux.statelength(st)
-
-        y, _ = l(g, x, ps, st)
-        @test Lux.outputsize(l) == (5,)
-        @test size(y) == (5, 10)
-        loss = (x, ps) -> sum(first(l(g, x, ps, st)))
-        @eval @test_gradients $loss $x $ps atol=1.0f-3 rtol=1.0f-3 skip_tracker=true skip_reverse_diff=true
+        test_lux_layer(rng, l, g, x, outputsize=(5,))
     end
 
     @testset "GraphConv" begin
         l = GraphConv(3 => 5, relu)
-        @test l isa GNNLayer
-        ps = Lux.initialparameters(rng, l)
-        st = Lux.initialstates(rng, l)
-        @test Lux.parameterlength(l) == Lux.parameterlength(ps)
-        @test Lux.statelength(l) == Lux.statelength(st)
-
-        y, _ = l(g, x, ps, st)
-        @test Lux.outputsize(l) == (5,)
-        @test size(y) == (5, 10)
-        loss = (x, ps) -> sum(first(l(g, x, ps, st)))
-        @eval @test_gradients $loss $x $ps atol=1.0f-3 rtol=1.0f-3 skip_tracker=true
+        test_lux_layer(rng, l, g, x, outputsize=(5,))
     end
 
     @testset "AGNNConv" begin
         l = AGNNConv(init_beta=1.0f0)
-        @test l isa GNNLayer
-        ps = Lux.initialparameters(rng, l)
-        st = Lux.initialstates(rng, l)
-        @test Lux.parameterlength(ps) == 1
-        @test Lux.parameterlength(l) == Lux.parameterlength(ps)
-        @test Lux.statelength(l) == Lux.statelength(st)
-
-        y, _ = l(g, x, ps, st)
-        @test size(y) == size(x)
-        loss = (x, ps) -> sum(first(l(g, x, ps, st)))
-        @eval @test_gradients $loss $x $ps atol=1.0f-3 rtol=1.0f-3 skip_tracker=true skip_reverse_diff=true
+        test_lux_layer(rng, l, g, x, sizey=(3,10))
     end
 
     @testset "EdgeConv" begin
         nn = Chain(Dense(6 => 5, relu), Dense(5 => 5))
         l = EdgeConv(nn, aggr = +)
-        @test l isa GNNContainerLayer
-        ps = Lux.initialparameters(rng, l)
-        st = Lux.initialstates(rng, l)
-        @test Lux.parameterlength(l) == Lux.parameterlength(ps)
-        @test Lux.statelength(l) == Lux.statelength(st)
-        y, st′ = l(g, x, ps, st)
-        @test size(y) == (5, 10)
-        loss = (x, ps) -> sum(first(l(g, x, ps, st)))
-        @eval @test_gradients $loss $x $ps atol=1.0f-3 rtol=1.0f-3 skip_tracker=true skip_reverse_diff=true
+        test_lux_layer(rng, l, g, x, sizey=(5,10), container=true)
     end
 
     @testset  "CGConv" begin
-        l = CGConv(3 => 5, residual = true)
-        @test l isa GNNContainerLayer
-        ps = Lux.initialparameters(rng, l)
-        st = Lux.initialstates(rng, l)
-        @test Lux.parameterlength(l) == Lux.parameterlength(ps)
-        @test Lux.statelength(l) == Lux.statelength(st)
-        y, st′ = l(g, x, ps, st)
-        @test size(y) == (5, 10)
-        @test Lux.outputsize(l) == (5,)
-        loss = (x, ps) -> sum(first(l(g, x, ps, st)))
-        @eval @test_gradients $loss $x $ps atol=1.0f-3 rtol=1.0f-3 skip_tracker=true
+        l = CGConv(3 => 3, residual = true)
+        test_lux_layer(rng, l, g, x, outputsize=(3,), container=true)
     end
 end
diff --git a/GNNLux/test/shared_testsetup.jl b/GNNLux/test/shared_testsetup.jl
index 2ba29ea1a..1354ef387 100644
--- a/GNNLux/test/shared_testsetup.jl
+++ b/GNNLux/test/shared_testsetup.jl
@@ -2,23 +2,43 @@
 
 import Reexport: @reexport
 
+@reexport using Test
 @reexport using GNNLux
-@reexport using Lux, Functors
-@reexport using ComponentArrays, LuxCore, LuxTestUtils, Random, StableRNGs, Test,
-                Zygote, Statistics
-@reexport using LuxTestUtils: @jet, @test_gradients, check_approx
-using MLDataDevices
-
-# Some Helper Functions
-function get_default_rng(mode::String)
-    dev = mode == "cpu" ? CPUDevice() :
-          mode == "cuda" ? CUDADevice() : mode == "amdgpu" ? AMDGPUDevice() : nothing
-    rng = default_device_rng(dev)
-    return rng isa TaskLocalRNG ? copy(rng) : deepcopy(rng)
-end
+@reexport using Lux
+@reexport using StableRNGs
+@reexport using Random, Statistics
+
+using LuxTestUtils: test_gradients, AutoReverseDiff, AutoTracker, AutoForwardDiff, AutoEnzyme
+
+export test_lux_layer
 
-export get_default_rng
+function test_lux_layer(rng::AbstractRNG, l, g::GNNGraph, x; 
+            outputsize=nothing, sizey=nothing, container=false,
+            atol=1.0f-2, rtol=1.0f-2)
 
-# export BACKEND_GROUP, MODES, cpu_testing, cuda_testing, amdgpu_testing
+    if container
+        @test l isa GNNContainerLayer
+    else
+        @test l isa GNNLayer
+    end
+
+    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)
+    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
 
 end
\ No newline at end of file