def _process_movie_fea(self):
        """

        Parameters
        ----------
        movie_info : pd.DataFrame
        name :  str

        Returns
        -------
        movie_features : np.ndarray
            Generate movie features by concatenating embedding and the year

        """
        import torchtext
        from torchtext.data.utils import get_tokenizer

        if self._name == "ml-100k":
            GENRES = GENRES_ML_100K
        elif self._name == "ml-1m":
            GENRES = GENRES_ML_1M
        elif self._name == "ml-10m":
            GENRES = GENRES_ML_10M
        else:
            raise NotImplementedError

        # Old torchtext-legacy API commented below
        # TEXT = torchtext.legacy.data.Field(tokenize='spacy', tokenizer_language='en_core_web_sm')
        tokenizer = get_tokenizer(
            "spacy", language="en_core_web_sm"
        )  # new API (torchtext 0.9+)
        embedding = torchtext.vocab.GloVe(name="840B", dim=300)

        title_embedding = np.zeros(
            shape=(self.movie_info.shape[0], 300), dtype=np.float32
        )
        release_years = np.zeros(
            shape=(self.movie_info.shape[0], 1), dtype=np.float32
        )
        p = re.compile(r"(.+)\s*\((\d+)\)")
        for i, title in enumerate(self.movie_info["title"]):
            match_res = p.match(title)
            if match_res is None:
                print(
                    "{} cannot be matched, index={}, name={}".format(
                        title, i, self._name
                    )
                )
                title_context, year = title, 1950
            else:
                title_context, year = match_res.groups()
            # We use average of glove
            # Upgraded torchtext API:  TEXT.tokenize(title_context) --> tokenizer(title_context)
            title_embedding[i, :] = (
                embedding.get_vecs_by_tokens(tokenizer(title_context))
                .numpy()
                .mean(axis=0)
            )
            release_years[i] = float(year)
        movie_features = np.concatenate(
            (
                title_embedding,
                (release_years - 1950.0) / 100.0,
                self.movie_info[GENRES],
            ),
            axis=1,
        )
        return movie_features


        Parameters
        ----------
        movie_info : pd.DataFrame
        name :  str

        Returns
        -------
        movie_features : np.ndarray
            Generate movie features by concatenating embedding and the year

        
examples/pytorch/gcmc/data.py