RandomLinkSplit not working with HeteroData - graph

Question

I am having some serious trouble with torch-geometric when dealing with my own data.
I am trying to build a graph that has 4 different node entities (of which only 1 bears some node features, the others are simple nodes), and 5 different edge type (of which only one bears a weight).
I have managed to do so by building a HeteroData() object and loading the different matrices with labels, attributes and so on.
The problem arises when I try to call RandomLinkSplit. Here's what my call looks like:
import torch_geometric.transforms as T
transform = T.RandomLinkSplit(
num_val = 0.1,
num_test = 0.1,
edge_types = [('Patient', 'suffers_from', 'Diagnosis'),
('bla', 'bla', 'bla') #I copy all the edge types here
],
)
but I get the empty AssertionError on the condition:
assert is instance(rev_edge_types, list)
So I thought that I needed to transform the graph to undirected (for some weird reason) like the tutorial does, and then to sample also reverse edges (even though I don't need them):
import torch_geometric.transforms as T
data = T.ToUndirected()(data)
transform = T.RandomLinkSplit(
num_val = 0.1,
num_test = 0.1,
edge_types = [('Patient', 'suffers_from', 'Diagnosis'),
('bla', 'bla', 'bla') #I copy all the edge types here
],
rev_edge_types = [('Diagnosis', 'rev_suffers_from', 'Patient'),
...
]
)
but this time I get the error unsupported operand type(s) for *: 'Tensor' and 'NoneType'.
Does any expert have any ideas on why this is happening? I am simply trying to do a train test split, and from the docs I read the Heterogeneous graphs should be well supported, but I don't understand why this is not working and I have been trying different things for quite a lot of time.
Any help would be appreciated!
Thanks