code: def _check_vector_update(self, tokens, new_embedding):
          
          assert self._idx_to_vec is not None, '`idx_to_vec` has not been initialized.'
  
          if not isinstance(tokens, (list, tuple)) or len(tokens) == 1:
              assert isinstance(new_embedding, nd.NDArray) and len(new_embedding.shape) in [1, 2], \
                  '`new_embedding` must be a 1-D or 2-D NDArray if `tokens` is a single token.'
              if not isinstance(tokens, (list, tuple)):
                  tokens = [tokens]
              if len(new_embedding.shape) == 1:
                  new_embedding = new_embedding.expand_dims(0)
  
          else:
              assert isinstance(new_embedding, nd.NDArray) and len(new_embedding.shape) == 2, \
                  '`new_embedding` must be a 2-D NDArray if `tokens` is a list of multiple strings.'
          if self._idx_to_vec is not None:
              assert new_embedding.shape == (len(tokens), self._idx_to_vec.shape[1]), \
                  'The length of `new_embedding` must be equal to the number ' \
                  'of tokens and the width of new_embedding must be equal ' \
                  'to the dimension of embedding of the glossary.'
          else:
              assert new_embedding.shape[0] == len(tokens), \
                  'The length of `new_embedding` must be equal to the number of tokens'
          return tokens
docs: Check that tokens and embedding are in the format for __setitem__.