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Makes functions in `vak.transforms.distance.functional` return tensors so we don't cause errors when lightning tries to convert from numpy to tensors to log. Letting lightning do the conversion kind of works, but it can cause a fatal error for someone using an Apple M1 with 'mps' as the accelerator, see https://forum.vocalpy.org/t/vak-tweetynet-with-an-apple-m1-max/78/4?u=nicholdav I don't find any explicit statement in either the Lightning or Torchmetrics docs that metrics should always be tensors, and that this guarantees there won't be weird issues (right now we get a warning on start-up that all logged scalars should be float32, but I would expect one should be able to log integers too?). But from various issues I read, it seems like that should be the case, Lightning-AI/pytorch-lightning#2143 and I notice that torchmetrics classes tend to do things like convert to a float tensor
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