def METHOD_NAME():
    x_dict = {
        'author': torch.randn(6, 16),
        'paper': torch.empty(0, 12),
    }
    edge_index_dict = {
        ('paper', 'to', 'author'): torch.empty((2, 0), dtype=torch.long),
        ('author', 'to', 'paper'): torch.empty((2, 0), dtype=torch.long),
        ('paper', 'to', 'paper'): torch.empty((2, 0), dtype=torch.long),
    }
    metadata = (list(x_dict.keys()), list(edge_index_dict.keys()))
    in_channels = {'author': 16, 'paper': 12}
    conv = HANConv(in_channels, 16, metadata, heads=2)
    out_dict = conv(x_dict, edge_index_dict)
    assert len(out_dict) == 2
    assert out_dict['author'].size() == (6, 16)
    assert torch.all(out_dict['author'] == 0)
    assert out_dict['paper'].size() == (0, 16)