Convert heterogeneous NetworkX graph to HeteroData without normalizing features across node types

🚀 The feature, motivation and pitch

I am working on converting a heterogeneous NetworkX graph to HeteroData. Here is an example that I would like to see working:

nodes_types = torch.tensor([1, 2, 3])
nx_graph = nx.MultiGraph()
nx_graph.add_node(0, type=1, foo=4.97, bar=torch.tensor([9, 8]))
nx_graph.add_node(1, type=2, foo=4.97)
nx_graph.add_node(2, type=3)
pyg.utils.from_networkx(nx_graph, group_node_attrs=['type', 'foo', 'bar'])

which currently throws raise ValueError('Not all nodes contain the same attributes').

I know that my graph is heterogeneous, but to convert from NetworkX to HeteroData, I need to manually normalize all of the node attributes. I would like to not have to write my own conversion layer.

Alternatives

N/A

Additional context

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