def METHOD_NAME():
    x1 = torch.randn(4, 16)
    x2 = torch.randn(2, 16)
    edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
    adj1 = to_torch_csc_tensor(edge_index, size=(4, 4))
    nn = Seq(Lin(32, 16), ReLU(), Lin(16, 32))
    conv = EdgeConv(nn)
    assert str(conv) == (
        'EdgeConv(nn=Sequential(\n'
        '  (0): Linear(in_features=32, out_features=16, bias=True)\n'
        '  (1): ReLU()\n'
        '  (2): Linear(in_features=16, out_features=32, bias=True)\n'
        '))')
    out = conv(x1, edge_index)
    assert out.size() == (4, 32)
    assert torch.allclose(conv((x1, x1), edge_index), out, atol=1e-6)
    assert torch.allclose(conv(x1, adj1.t()), out, atol=1e-6)
    assert torch.allclose(conv((x1, x1), adj1.t()), out, atol=1e-6)
    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, atol=1e-6)
        assert torch.allclose(conv((x1, x1), adj2.t()), out, atol=1e-6)
    if is_full_test():
        t = '(Tensor, Tensor) -> Tensor'
        jit = torch.jit.script(conv.jittable(t))
        assert torch.allclose(jit(x1, edge_index), out, atol=1e-6)
        t = '(PairTensor, Tensor) -> Tensor'
        jit = torch.jit.script(conv.jittable(t))
        assert torch.allclose(jit((x1, x1), edge_index), out, atol=1e-6)
    if is_full_test() and torch_geometric.typing.WITH_TORCH_SPARSE:
        t = '(Tensor, SparseTensor) -> Tensor'
        jit = torch.jit.script(conv.jittable(t))
        assert torch.allclose(jit(x1, adj2.t()), out, atol=1e-6)
        t = '(PairTensor, SparseTensor) -> Tensor'
        jit = torch.jit.script(conv.jittable(t))
        assert torch.allclose(jit((x1, x1), adj2.t()), out, atol=1e-6)
    # Test bipartite message passing:
    adj1 = to_torch_csc_tensor(edge_index, size=(4, 2))
    out = conv((x1, x2), edge_index)
    assert out.size() == (2, 32)
    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) -> 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) -> Tensor'
        jit = torch.jit.script(conv.jittable(t))
        assert torch.allclose(jit((x1, x2), adj2.t()), out, atol=1e-6)