code: def _set_idx_to_vec_by_embeddings(self, token_embeddings, vocab_len, vocab_idx_to_token):
          
  
          new_vec_len = sum(embed.vec_len for embed in token_embeddings)
          new_idx_to_vec = nd.zeros(shape=(vocab_len, new_vec_len))
  
          col_start = 0
          # Concatenate all the embedding vectors in token_embeddings.
          for embed in token_embeddings:
              col_end = col_start + embed.vec_len
              # Cancatenate vectors of the unknown token.
              new_idx_to_vec[0, col_start:col_end] = embed.idx_to_vec[0]
              new_idx_to_vec[1:, col_start:col_end] = embed.get_vecs_by_tokens(vocab_idx_to_token[1:])
              col_start = col_end
  
          self._vec_len = new_vec_len
          self._idx_to_vec = new_idx_to_vec
docs: Sets the mapping between token indices and token embedding vectors.
  
  
          Parameters
          ----------
          token_embeddings : instance or list `mxnet.contrib.text.embedding._TokenEmbedding`
              One or multiple pre-trained token embeddings to load. If it is a list of multiple
              embeddings, these embedding vectors will be concatenated for each token.
          vocab_len : int
              Length of vocabulary whose tokens are indexed in the token embedding.
          vocab_idx_to_token: list of str
              A list of indexed tokens in the vocabulary. These tokens are indexed in the token
              embedding.