def METHOD_NAME():
    x = torch.randn(4, 8)
    edge_index = torch.tensor([[0, 1, 2, 3], [1, 0, 1, 1]])
    edge_weight = torch.randn(edge_index.size(1))
    edge_attr = torch.randn(edge_index.size(1), 4)
    conv = GATConv(8, 32, heads=2, edge_dim=1, fill_value=0.5)
    out = conv(x, edge_index, edge_weight)
    assert out.size() == (4, 64)
    if torch_geometric.typing.WITH_TORCH_SPARSE:
        adj1 = SparseTensor.from_edge_index(edge_index, edge_weight, (4, 4))
        with pytest.raises(NotImplementedError):
            assert torch.allclose(conv(x, adj1.t()), out)
    conv = GATConv(8, 32, heads=2, edge_dim=1, fill_value='mean')
    out = conv(x, edge_index, edge_weight)
    assert out.size() == (4, 64)
    if torch_geometric.typing.WITH_TORCH_SPARSE:
        with pytest.raises(NotImplementedError):
            assert torch.allclose(conv(x, adj1.t()), out)
    conv = GATConv(8, 32, heads=2, edge_dim=4, fill_value=0.5)
    out = conv(x, edge_index, edge_attr)
    assert out.size() == (4, 64)
    if torch_geometric.typing.WITH_TORCH_SPARSE:
        adj2 = SparseTensor.from_edge_index(edge_index, edge_attr, (4, 4))
        with pytest.raises(NotImplementedError):
            assert torch.allclose(conv(x, adj2.t()), out)
    conv = GATConv(8, 32, heads=2, edge_dim=4, fill_value='mean')
    out = conv(x, edge_index, edge_attr)
    assert out.size() == (4, 64)
    if torch_geometric.typing.WITH_TORCH_SPARSE:
        with pytest.raises(NotImplementedError):
            assert torch.allclose(conv(x, adj2.t()), out)