def METHOD_NAME(
    self,
    ssr: float,
):
    model = SimpleModel()
    optimizers = torch.optim.SGD(model.parameters(), lr=1.0)
    trainer = Trainer(
        model=model,
        train_dataloader=DataLoader(RandomClassificationDataset()),
        optimizers=optimizers,
        schedulers=[MultiStepScheduler(milestones=['30ba', '50ba'], gamma=0.1)],
        scale_schedule_ratio=ssr,
        callbacks=[CheckScaleSchedule(ssr)],
        max_duration='10ep',
    )
    trainer.state.train_metrics = {}  # avoid metrics construction
    assert trainer.state.max_duration is not None
    assert trainer.state.max_duration.unit == TimeUnit.EPOCH
    assert trainer.state.max_duration.value == int(10 * ssr)
    trainer.fit()