src/transformers/trainer.py/Trainer/_report_to_hp_search
class Trainer:    def _report_to_hp_search(
        self, trial: Union["optuna.Trial", Dict[str, Any]], epoch: int, metrics: Dict[str, float]
    ):
        if self.hp_search_backend is None or trial is None:
            return
        self.objective = self.compute_objective(metrics.copy())
        if self.hp_search_backend == HPSearchBackend.OPTUNA:
            import optuna

            trial.report(self.objective, epoch)
            if trial.should_prune():
                raise optuna.TrialPruned()
        elif self.hp_search_backend == HPSearchBackend.RAY:
            from ray import tune

            if self.control.should_save:
                self._tune_save_checkpoint()
            tune.report(objective=self.objective, **metrics)