code: def init_word_embs_model(
      model_path,
      model_type,
      force_reload=False,
      top_k=None,
      skip_check=False,
  ):
      global WORD_EMBS_MODELS
  
      if model_type in WORD_EMBS_MODELS and not force_reload:
          WORD_EMBS_MODELS[model_type].top_k = top_k
          return WORD_EMBS_MODELS[model_type]
  
      if model_type == "word2vec":
          model = nmw.Word2vec(top_k=top_k, skip_check=skip_check)
          model.read(model_path)
      elif model_type == "glove":
          model = nmw.GloVe(top_k=top_k, skip_check=skip_check)
          model.read(model_path)
      elif model_type == "fasttext":
          model = nmw.Fasttext(top_k=top_k, skip_check=skip_check)
          model.read(model_path)
      else:
          raise ValueError(
              "Model type value is unexpected. Expected values include {}".format(
                  model_types
              )
          )
  
      WORD_EMBS_MODELS[model_type] = model
      return model
  
file_path: nlpaug/nlpaug/augmenter/word/word_embs.py
imports: from nlpaug.augmenter.word import WordAugmenter
  from nlpaug.util import Action, Doc
  import nlpaug.model.word_embs
label_desc: Use image node_id 1 for calling a global function with example usage: init_word_embs_model(model_path, model_type, force_reload, top_k, skip_check) and returns: model, WORD_EMBS_MODELS