I'd like to calculate [Word Mover's Distance](http://proceedings.mlr.press/v37/kusnerb15.pdf) with [Universal Sentence Encoder on TensorFlow Hub](https://tfhub.dev/google/universal-sentence-encoder/2) embedding. 

I have tried the example on [spaCy for WMD-relax](https://spacy.io/universe/project/wmd-relax), which loads 'en' model from spaCy, but I couldn't find another way to feed other embeddings. 

In [gensim](https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/WMD_tutorial.ipynb), it seems that it only accepts `load_word2vec_format` file (`file.bin`) or `load` file (`file.vec`). 

As I know, someone has written a [Bert to token embeddings based on pytorch](https://github.com/Kyubyong/bert-token-embeddings), but it's not generalized to other models on tf-hub.

Is there any other approach to transfer pretrained models on tf-hub to spaCy format or word2vec format?