def METHOD_NAME(self):
    model_save = ModelUnderTest(bias=False)
    mask = torch.eye(16)
    parametrize.register_parametrization(model_save.linear, 'weight',
                                         utils.FakeSparsity(mask))
    mask = torch.eye(16)
    parametrize.register_parametrization(model_save.seq[0], 'weight',
                                         utils.FakeSparsity(mask))
    mask = torch.eye(16)
    parametrize.register_parametrization(model_save.seq[1], 'weight',
                                         utils.FakeSparsity(mask))
    state_dict = model_save.state_dict()
    model_load = ModelUnderTest(bias=False)
    mask = torch.zeros(model_load.linear.weight.shape)
    parametrize.register_parametrization(model_load.linear, 'weight',
                                         utils.FakeSparsity(mask))
    mask = torch.zeros(model_load.seq[0].weight.shape)
    parametrize.register_parametrization(model_load.seq[0], 'weight',
                                         utils.FakeSparsity(mask))
    mask = torch.zeros(model_load.seq[1].weight.shape)
    parametrize.register_parametrization(model_load.seq[1], 'weight',
                                         utils.FakeSparsity(mask))
    # Keep this strict, as we are not loading the 'mask'
    model_load.load_state_dict(state_dict, strict=False)
    # Check the parametrizations are preserved
    assert hasattr(model_load.linear, 'parametrizations')
    assert parametrize.is_parametrized(model_load.linear, 'weight')
    assert hasattr(model_load.seq[0], 'parametrizations')
    assert parametrize.is_parametrized(model_load.linear, 'weight')
    assert hasattr(model_load.seq[1], 'parametrizations')
    assert parametrize.is_parametrized(model_load.linear, 'weight')
    # Check the weigths are preserved
    self.assertEqual(model_save.linear.parametrizations['weight'].original,
                     model_load.linear.parametrizations['weight'].original)
    self.assertEqual(model_save.seq[0].parametrizations['weight'].original,
                     model_load.seq[0].parametrizations['weight'].original)
    self.assertEqual(model_save.seq[1].parametrizations['weight'].original,
                     model_load.seq[1].parametrizations['weight'].original)
    # Check the masks are not preserved in the state_dict
    # We store the state_dicts in the sparsifier, not in the model itself.
    # TODO: Need to find a clean way of exporting the parametrized model
    self.assertNotEqual(model_save.linear.parametrizations['weight'][0].mask,
                        model_load.linear.parametrizations['weight'][0].mask)
    self.assertNotEqual(model_save.seq[0].parametrizations['weight'][0].mask,
                        model_load.seq[0].parametrizations['weight'][0].mask)
    self.assertNotEqual(model_save.seq[1].parametrizations['weight'][0].mask,
                        model_load.seq[1].parametrizations['weight'][0].mask)