Load the model from a file.

        The model is loaded from an XGBoost internal binary format which is
        universal among the various XGBoost interfaces. Auxiliary attributes of
        the Python Booster object (such as feature names) will not be loaded.
        Label encodings (text labels to numeric labels) will be also lost.
        **If you are using only the Python interface, we recommend pickling the
        model object for best results.**

        Parameters
        ----------
        fname : string or a memory buffer
            Input file name or memory buffer(see also save_raw)

def load_model(self, fname):
        """
        Load the model from a file.

        The model is loaded from an XGBoost internal binary format which is
        universal among the various XGBoost interfaces. Auxiliary attributes of
        the Python Booster object (such as feature names) will not be loaded.
        Label encodings (text labels to numeric labels) will be also lost.
        **If you are using only the Python interface, we recommend pickling the
        model object for best results.**

        Parameters
        ----------
        fname : string or a memory buffer
            Input file name or memory buffer(see also save_raw)
        """
        if self._Booster is None:
            self._Booster = Booster({'nthread': self.n_jobs})
        self._Booster.load_model(fname)