def METHOD_NAME():
    x = torch.randn(4, 16)
    edge_index = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
    conv = GCNConv(16, 32)
    decomposed_conv = copy.deepcopy(conv)
    decomposed_conv.decomposed_layers = 2
    out1 = conv(x, edge_index)
    out2 = decomposed_conv(x, edge_index)
    assert torch.allclose(out1, out2)
    if is_full_test():
        t = '(Tensor, Tensor, OptTensor) -> Tensor'
        jit = torch.jit.script(decomposed_conv.jittable(t))
        assert torch.allclose(jit(x, edge_index), out1)