You could map 2 to a made-up probability (0.5 or maybe the fraction positive / (positive + negative)).
Alternatively, you could mask the uncertain labels (i.e. the network can predict what it wants there).
Then you have one target and one label per class, just like BCEWithLogitsLoss wants.
Best regards
Thomas