def METHOD_NAME():
    config = get_empty_config()
    config["compression"] = {
        "algorithm": "magnitude_sparsity",
        "params": {"weight_importance": "abs", "schedule": "multistep", "multistep_sparsity_levels": [0.3, 0.5]},
    }
    sparse_model, _ = create_compressed_model_and_algo_for_test(MagnitudeTestModel(), config)
    with torch.no_grad():
        sparse_model(torch.ones([1, 1, 10, 10]))
    config = get_empty_config()
    config["compression"] = {"algorithm": "const_sparsity"}
    const_sparse_model, _ = create_compressed_model_and_algo_for_test(MagnitudeTestModel(), config)
    load_state(const_sparse_model, sparse_model.state_dict())
    op = const_sparse_model.conv1.pre_ops["0"]
    PTTensorListComparator.check_equal(ref_mask_1, op.operand.binary_mask)
    op = const_sparse_model.conv2.pre_ops["0"]
    PTTensorListComparator.check_equal(ref_mask_2, op.operand.binary_mask)