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It is expected that hnsw sometimes misses one neighbor, which has an
impact on the DTM, especially if the number of neighbors considered is
low.
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torch.isnan(None) raises an exception anyway
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This can easily happen with pytorch, and there is special code to avoid
it.
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This doesn't seem like the best way to handle it, we may want to handle
it like a wrapper that gets the indices from knn (whatever backend) and
then computes the distances.
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It is supposed to be possible to compile numpy with openmp, but it looks
like it isn't done in any of the usual packages.
It may be possible to refactor that code so there is less redundancy.
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