How to use the contextualbandits.utils._OneVsRest function in contextualbandits

Parameters
        ----------
        X : array (n_samples, n_features)
            Matrix of covariates for the available data.
        a : array (n_samples), int type
            Arms or actions that were chosen for each observations.
        r : array (n_samples), {0,1}
            Rewards that were observed for the chosen actions. Must be binary rewards 0/1.

        Returns
        -------
        self : obj
            This object
        """
        X, a, r = _check_fit_input(X, a, r, self.choice_names)
        self._oracles = _OneVsRest(self.base_algorithm,
                                   X, a, r,
                                   self.nchoices,
                                   self.beta_prior[1], self.beta_prior[0][0], self.beta_prior[0][1],
                                   self.smoothing,
                                   self.assume_unique_reward,
                                   self.batch_train,
                                   njobs = self.njobs)
        self.is_fitted = True
        return self

Parameters
        ----------
        X : array (n_samples, n_features)
            Matrix of covariates for the available data.
        a : array (n_samples), int type
            Arms or actions that were chosen for each observations.
        r : array (n_samples), {0,1}
            Rewards that were observed for the chosen actions. Must be binary rewards 0/1.

        Returns
        -------
        self : obj
            This object
        """
        X, a, r = _check_fit_input(X, a, r, self.choice_names)
        self._oracles = _OneVsRest(self.base_algorithm,
                                   X, a, r,
                                   self.nchoices,
                                   self.beta_prior[1], self.beta_prior[0][0], self.beta_prior[0][1],
                                   self.smoothing,
                                   self.assume_unique_reward,
                                   self.batch_train,
                                   self._force_fit,
                                   force_counters = self.active_choice is not None,
                                   njobs = self.njobs)
        self.is_fitted = True
        return self

Parameters
        ----------
        X : array (n_samples, n_features)
            Matrix of covariates for the available data.
        a : array (n_samples), int type
            Arms or actions that were chosen for each observations.
        r : array (n_samples), {0,1}
            Rewards that were observed for the chosen actions. Must be binary rewards 0/1.

        Returns
        -------
        self : obj
            This object
        """
        self._oracles = _OneVsRest(self.base_algorithm,
                                   X, a, r,
                                   self.nchoices,
                                   self.beta_prior[1], self.beta_prior[0][0], self.beta_prior[0][1],
                                   self.smoothing,
                                   self.assume_unique_reward,
                                   self.batch_train,
                                   force_fit = self._force_fit,
                                   force_counters = True,
                                   njobs = self.njobs)
        self.is_fitted = True
        return self