def METHOD_NAME():
    model = Sequential('x, edge_index', [
        (SAGEConv((-1, -1), 32), 'x, edge_index -> x1'),
        ReLU(),
        (SAGEConv((-1, -1), 64), 'x1, edge_index -> x2'),
        ReLU(),
    ])
    x_dict = {
        'paper': torch.randn(100, 16),
        'author': torch.randn(100, 16),
    }
    edge_index_dict = {
        ('paper', 'cites', 'paper'):
        torch.randint(100, (2, 200), dtype=torch.long),
        ('paper', 'written_by', 'author'):
        torch.randint(100, (2, 200), dtype=torch.long),
        ('author', 'writes', 'paper'):
        torch.randint(100, (2, 200), dtype=torch.long),
    }
    metadata = list(x_dict.keys()), list(edge_index_dict.keys())
    model = to_hetero(model, metadata, debug=False)
    out_dict = model(x_dict, edge_index_dict)
    assert isinstance(out_dict, dict) and len(out_dict) == 2
    assert out_dict['paper'].size() == (100, 64)
    assert out_dict['author'].size() == (100, 64)