code: def latent_semantic_scale_from_word2vec(
      model,
      pos_seed_words=None,
      neg_seed_words=None,
      seed_words=None,
      seed_values=None,
  ):
      terms = [word for word in model.wv.key_to_index.keys()]
      embeddings = np.matrix(
          [model.wv[word] for word in model.wv.key_to_index.keys()]
      )
      return lss_terms(
          embeddings,
          terms,
          pos_seed_words,
          neg_seed_words,
          seed_words,
          seed_values,
      )
  
imports: import logging
  import numpy
  import pandas
label_desc: Use image node_id 1 for calling a global function with example usage: latent_semantic_scale_from_word2vec(model, pos_seed_words, neg_seed_words, seed_words, seed_values) and returns: lss_terms