def METHOD_NAME(batch_norm):
    x1 = torch.randn(4, 8)
    x2 = torch.randn(2, 16)
    edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
    adj1 = to_torch_csr_tensor(edge_index, size=(4, 4))
    conv = CGConv(8, batch_norm=batch_norm)
    assert str(conv) == 'CGConv(8, dim=0)'
    out = conv(x1, edge_index)
    assert out.size() == (4, 8)
    assert torch.allclose(conv(x1, adj1.t()), out)
    if torch_geometric.typing.WITH_TORCH_SPARSE:
        adj2 = SparseTensor.from_edge_index(edge_index, sparse_sizes=(4, 4))
        assert torch.allclose(conv(x1, adj2.t()), out)
    if is_full_test():
        t = '(Tensor, Tensor, OptTensor) -> Tensor'
        jit = torch.jit.script(conv.jittable(t))
        assert torch.allclose(jit(x1, edge_index), out, atol=1e-6)
    if is_full_test() and torch_geometric.typing.WITH_TORCH_SPARSE:
        t = '(Tensor, SparseTensor, OptTensor) -> Tensor'
        jit = torch.jit.script(conv.jittable(t))
        assert torch.allclose(jit(x1, adj2.t()), out, atol=1e-6)
    # Test bipartite message passing:
    adj1 = to_torch_csr_tensor(edge_index, size=(4, 2))
    conv = CGConv((8, 16))
    assert str(conv) == 'CGConv((8, 16), dim=0)'
    out = conv((x1, x2), edge_index)
    assert out.size() == (2, 16)
    assert torch.allclose(conv((x1, x2), adj1.t()), out, atol=1e-6)
    if torch_geometric.typing.WITH_TORCH_SPARSE:
        adj2 = SparseTensor.from_edge_index(edge_index, sparse_sizes=(4, 2))
        assert torch.allclose(conv((x1, x2), adj2.t()), out, atol=1e-6)
    if is_full_test():
        t = '(PairTensor, Tensor, OptTensor) -> Tensor'
        jit = torch.jit.script(conv.jittable(t))
        assert torch.allclose(jit((x1, x2), edge_index), out, atol=1e-6)
    if is_full_test() and torch_geometric.typing.WITH_TORCH_SPARSE:
        t = '(PairTensor, SparseTensor, OptTensor) -> Tensor'
        jit = torch.jit.script(conv.jittable(t))
        assert torch.allclose(jit((x1, x2), adj2.t()), out, atol=1e-6)