def METHOD_NAME():
    model = get_magnitude_test_model()
    config = get_basic_magnitude_sparsity_config()
    config["compression"]["params"] = {"schedule": "multistep"}
    sparse_model, compression_ctrl = create_compressed_model_and_algo_for_test(model, config)
    assert isinstance(compression_ctrl, MagnitudeSparsityController)
    assert compression_ctrl.scheduler.current_sparsity_level == approx(0.1)
    conv_names = [layer.name for layer in model.layers if isinstance(layer, tf.keras.layers.Conv2D)]
    wrappers = [layer for layer in sparse_model.layers if isinstance(layer, NNCFWrapper)]
    correct_wrappers = [wrapper for wrapper in wrappers if wrapper.name in conv_names]
    assert len(conv_names) == len(wrappers)
    assert len(conv_names) == len(correct_wrappers)
    assert compression_ctrl._threshold == approx(0.24, 0.1)  # pylint: disable=protected-access
    # pylint: disable=protected-access
    assert isinstance(compression_ctrl._weight_importance_fn, type(normed_magnitude))
    for i, wrapper in enumerate(wrappers):
        ref_mask = tf.ones_like(wrapper.weights[-1]) if i == 0 else ref_mask_2
        mask = list(wrapper.ops_weights.values())[0]["mask"]
        op = list(wrapper.weights_attr_ops["kernel"].values())[0]
        tf.assert_equal(mask, ref_mask)
        assert isinstance(op, BinaryMask)