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.

def _load_embedding(self, pretrained_file_path, elem_delim,
                        encoding='utf8'):
        """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.

        """

        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)