NODE_WORD_FILENAME = "word2vec.emb"
    NODE_MODEL_FILENAME = "word2vec.model"
    EDGES_WORD_FILENAME = "edges2vec.emb"

    if args.method=="train":

        # Precompute probabilities and generate walks
        node2vec = Node2Vec(G, dimensions=64, walk_length=30, num_walks=200,workers=4)  # Use temp_folder for big graphs

        # Embed nodes
        model = node2vec.fit(window=10, min_count=1, batch_words=4)  # Any keywords acceptable by gensim.Word2Vec can be passed, `dimensions` and `workers` are automatically passed (from the Node2Vec constructor)

        # Save embeddings for later use
        model.wv.save_word2vec_format(NODE_WORD_FILENAME)

        # Save model for later use
        model.save(NODE_MODEL_FILENAME)

        edges_embs = HadamardEmbedder(keyed_vectors=model.wv)

        # Get all edges in a separate KeyedVectors instance - use with caution could be huge for big networks
        edges_kv = edges_embs.as_keyed_vectors()

        # Save embeddings for later use
        edges_kv.save_word2vec_format(EDGES_WORD_FILENAME)


    if args.method == "test":
        import re

        model = Word2Vec.load(NODE_MODEL_FILENAME)
        # this generates an error: could not convert string to float
        edges_kv = KeyedVectors.load_word2vec_format(EDGES_WORD_FILENAME)

  File "/home/robomotic/DevOps/gitlab/ava-prod-ai/venv/lib/python3.11/site-packages/gensim/models/keyedvectors.py", line 1980, in <listcomp>
    word, weights = parts[0], [datatype(x) for x in parts[1:]]

    edges_kv = KeyedVectors.load_word2vec_format(EDGES_WORD_FILENAME)