def METHOD_NAME():
    data = HeteroData()
    data['paper'].x = torch.randn(50, 32)
    data['paper'].y = torch.randn(50, 3)
    data['paper'].z = torch.randn(50, 3)
    data['author'].x = torch.randn(30, 64)
    data['author'].y = torch.randn(30, 3)
    data['author'].z = torch.randn(30, 3)
    data['paper', 'paper'].edge_index = get_random_edge_index(50, 50, 200)
    data['paper', 'author'].edge_index = get_random_edge_index(50, 30, 100)
    data['author', 'paper'].edge_index = get_random_edge_index(30, 50, 100)
    conv = HeteroConv({key: CustomConv(64) for key in data.edge_types})
    # Test node `args_dict` and `kwargs_dict` with `y_dict` and `z_dict`:
    out = conv(data.x_dict, data.edge_index_dict, data.y_dict,
               z_dict=data.z_dict)
    assert len(out) == 2
    assert out['paper'].size() == (50, 64)
    assert out['author'].size() == (30, 64)