code: def _load_pretrained_vocab(name, root=os.path.join(get_home_dir(), 'models'), cls=None):
      
      file_name = '{name}-{short_hash}'.format(name=name,
                                               short_hash=short_hash(name))
      root = os.path.expanduser(root)
      file_path = os.path.join(root, file_name + '.vocab')
      sha1_hash = _vocab_sha1[name]
      if os.path.exists(file_path):
          if check_sha1(file_path, sha1_hash):
              return _load_vocab_file(file_path, cls)
          else:
              print('Detected mismatch in the content of model vocab file. Downloading again.')
      else:
          print('Vocab file is not found. Downloading.')
  
      if not os.path.exists(root):
          os.makedirs(root)
  
      zip_file_path = os.path.join(root, file_name + '.zip')
      repo_url = _get_repo_url()
      if repo_url[-1] != '/':
          repo_url = repo_url + '/'
      download(_url_format.format(repo_url=repo_url, file_name=file_name),
               path=zip_file_path,
               overwrite=True)
      with zipfile.ZipFile(zip_file_path) as zf:
          zf.extractall(root)
      os.remove(zip_file_path)
  
      if check_sha1(file_path, sha1_hash):
          return _load_vocab_file(file_path, cls)
      else:
          raise ValueError('Downloaded file has different hash. Please try again.')
docs: Load the accompanying vocabulary object for pre-trained model.
  
      Parameters
      ----------
      name : str
          Name of the vocabulary, usually the name of the dataset.
      root : str, default '$MXNET_HOME/models'
          Location for keeping the model parameters.
          MXNET_HOME defaults to '~/.mxnet'.
      cls : nlp.Vocab or nlp.vocab.BERTVocab, default nlp.Vocab
  
      Returns
      -------
      Vocab or nlp.vocab.BERTVocab
          Loaded vocabulary object for the pre-trained model.