code: def load_embedding_from_path(args):
      
      if args.embedding_path.endswith('.bin'):
          with utils.print_time('load fastText model.'):
              model = \
                  nlp.model.train.FasttextEmbeddingModel.load_fasttext_format(
                      args.embedding_path)
          idx_to_token = sorted(model._token_to_idx, key=model._token_to_idx.get)
  
          embedding = nlp.embedding.TokenEmbedding(
              unknown_token=None, unknown_lookup=model, allow_extend=True)
  
          # Analogy task is open-vocabulary, so must keep all known words.
          # But if not evaluating analogy, no need to precompute now as all
          # words for closed vocabulary task can be obtained via the unknown
          # lookup
          if not args.analogy_datasets:
              idx_to_token = []
          elif args.analogy_datasets and args.analogy_max_vocab_size:
              idx_to_token = idx_to_token[:args.analogy_max_vocab_size]
  
          embedding['<unk>'] = mx.nd.zeros(model.weight.shape[1])
          if idx_to_token:
              with utils.print_time('compute vectors for {} known '
                                    'words.'.format(len(idx_to_token))):
                  embedding[idx_to_token] = model[idx_to_token]
      else:
          embedding = nlp.embedding.TokenEmbedding.from_file(args.embedding_path)
  
      return embedding
docs: Load a TokenEmbedding.