code: def _load_embedding(self, pretrained_file_path, elem_delim,
                          encoding='utf8'):
          
  
          pretrained_file_path = os.path.expanduser(pretrained_file_path)
  
          if not os.path.isfile(pretrained_file_path):
              raise ValueError('`pretrained_file_path` must be a valid path '
                               'to the pre-trained token embedding file.')
  
          logging.info('Loading pre-trained token embedding vectors from %s',
                       pretrained_file_path)
  
          if pretrained_file_path.endswith('.npz'):
              self._load_embedding_serialized(
                  pretrained_file_path=pretrained_file_path)
          else:
              self._load_embedding_txt(
                  pretrained_file_path=pretrained_file_path,
                  elem_delim=elem_delim, encoding=encoding)
docs: Load embedding vectors from a pre-trained token embedding file.
  
          Both text files and TokenEmbedding serialization files are supported.
          elem_delim and encoding are ignored for non-text files.
  
          For every unknown token, if its representation `self.unknown_token` is encountered in the
          pre-trained token embedding file, index 0 of `self.idx_to_vec` maps to the pre-trained token
          embedding vector loaded from the file; otherwise, index 0 of `self.idx_to_vec` maps to the
          text embedding vector initialized by `self._init_unknown_vec`.
  
          If a token is encountered multiple times in the pre-trained text embedding file, only the
          first-encountered token embedding vector will be loaded and the rest will be skipped.