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