code: def get_pret_embs(self, word_dims=None):
          
          assert (self._pret_embeddings is not None), "No pretrained file provided."
          pret_embeddings = gluonnlp.embedding.create(self._pret_embeddings[0], source=self._pret_embeddings[1])
          embs = [None] * len(self._id2word)
          for idx, vec in enumerate(pret_embeddings.idx_to_vec):
              embs[idx] = vec.asnumpy()
          if word_dims is None:
              word_dims = len(pret_embeddings.idx_to_vec[0])
          for idx, emb in enumerate(embs):
              if emb is None:
                  embs[idx] = np.zeros(word_dims)
          pret_embs = np.array(embs, dtype=np.float32)
          return pret_embs / np.std(pret_embs)
docs: Read pre-trained embedding file
  
          Parameters
          ----------
          word_dims : int or None
              vector size. Use `None` for auto-infer
          Returns
          -------
          numpy.ndarray
              T x C numpy NDArray