def METHOD_NAME(self):
    """Checks if the warning is thrown if the scheduler step is called
    before the sparsifier step"""
    model = nn.Sequential(
        nn.Linear(16, 16)
    )
    sparsifier = WeightNormSparsifier()
    sparsifier.prepare(model, config=None)
    scheduler = ImplementedScheduler(sparsifier)
    # Sparsifier step is not called
    with self.assertWarns(UserWarning):
        scheduler.step()
    # Correct order has no warnings
    # Note: This will trigger if other warnings are present.
    with warnings.catch_warnings(record=True) as w:
        sparsifier.step()
        scheduler.step()
        # Make sure there is no warning related to the base_scheduler
        for warning in w:
            fname = warning.filename
            fname = '/'.join(fname.split('/')[-5:])
            assert fname != 'torch/ao/sparsity/scheduler/base_scheduler.py'