How to use the suod.models.parallel_processes._parallel_predict_proba function in suod

else:
            # use simple equal split by sklearn
            n_estimators_list, starts, n_jobs = _partition_estimators(
                self.n_estimators, self.n_jobs)

        # fit the base models
        if self.verbose:
            print('Parallel score prediction...')
            start = time.time()

        # TODO: code cleanup. There is an existing bug for joblib on Windows:
        # https://github.com/joblib/joblib/issues/806
        # max_nbytes can be dropped on other OS
        all_results_scores = Parallel(n_jobs=n_jobs, max_nbytes=None,
                                      verbose=True)(
            delayed(_parallel_predict_proba)(
                n_estimators_list[i],
                self.base_estimators[starts[i]:starts[i + 1]],
                self.approximators[starts[i]:starts[i + 1]],
                X,
                self.n_estimators,
                # self.rp_flags[starts[i]:starts[i + 1]],
                self.jl_transformers_[starts[i]:starts[i + 1]],
                self.approx_flags[starts[i]:starts[i + 1]],
                verbose=True)
            for i in range(n_jobs))

        # fit the base models
        if self.verbose:
            print('Parallel Score Prediction without Approximators '
                  'Total Time:', time.time() - start)