How to use the poutyne.framework.callbacks.LRSchedulerCheckpoint function in Poutyne

def test_any_scheduler_checkpoints(self):
        lr_scheduler = ExponentialLR(gamma=0.01)
        checkpointer = LRSchedulerCheckpoint(lr_scheduler, self.checkpoint_filename, period=1)
        self._test_checkpointer(checkpointer, lr_scheduler)

def test_reduce_lr_checkpoints(self):
        reduce_lr = ReduceLROnPlateau(monitor='loss', patience=3)
        checkpointer = LRSchedulerCheckpoint(reduce_lr, self.checkpoint_filename, period=1)
        self._test_checkpointer(checkpointer, reduce_lr)

def test_reduce_lr_on_plateau_integration(self):
        train_gen = some_data_generator(OptimizerCheckpointTest.batch_size)
        valid_gen = some_data_generator(OptimizerCheckpointTest.batch_size)
        reduce_lr = ReduceLROnPlateau(monitor='loss', patience=3)
        checkpointer = LRSchedulerCheckpoint(reduce_lr, self.checkpoint_filename, period=1)
        self.model.fit_generator(train_gen,
                                 valid_gen,
                                 epochs=OptimizerCheckpointTest.epochs,
                                 steps_per_epoch=5,
                                 callbacks=[checkpointer])

def test_any_scheduler_integration(self):
        train_gen = some_data_generator(OptimizerCheckpointTest.batch_size)
        valid_gen = some_data_generator(OptimizerCheckpointTest.batch_size)
        lr_scheduler = ExponentialLR(gamma=0.01)
        checkpointer = LRSchedulerCheckpoint(lr_scheduler, self.checkpoint_filename, period=1)
        self.model.fit_generator(train_gen,
                                 valid_gen,
                                 epochs=OptimizerCheckpointTest.epochs,
                                 steps_per_epoch=5,
                                 callbacks=[checkpointer])

def _init_lr_scheduler_callbacks(self, lr_schedulers):
        callbacks = []
        if self.logging:
            for i, lr_scheduler in enumerate(lr_schedulers):
                filename = self.lr_scheduler_filename % i
                tmp_filename = self.lr_scheduler_tmp_filename % i
                callbacks += [
                    LRSchedulerCheckpoint(lr_scheduler, filename, verbose=False, temporary_filename=tmp_filename)
                ]
        else:
            callbacks += lr_schedulers
            callbacks += [BestModelRestore(monitor=self.monitor_metric, mode=self.monitor_mode, verbose=True)]
        return callbacks