def METHOD_NAME():
    # define the functor for evaluating the norm of the L1 error vector
    ref_table = "input/vlct/MHD_shock_tube/rj2a_shock_tube_t0.2_res256.csv"
    l1_func= CalcTableL1Norm(["density","velocity_x","velocity_y","velocity_z",
                              "pressure","bfield_x","bfield_y","bfield_z"],
                             default_ref_table = ref_table)
    err_compare = partial(analyze_shock,
                          target_template = "method_vlct-1-{:s}_rj2a_N256_0.2",
                          name_template = "{}-axis rj2a shock tube N=256",
                          l1_functor = l1_func)
    r = []
    # check the average L1-Norm along the active axis (averaged over multiple
    # slices along the active axis)
    # check that the standard deviation of the L1-Norms computed along the
    # active axis (There should be no definitely be no differences if we only
    # use 1 block - if we use more than one block, it's unclear to me if it's
    # ok to have round-off errors)
    r.append(err_compare(0.012523489882320429, "x"))
    r.append(err_compare(0.0, "x", std_dev=True))
    r.append(err_compare(0.012523489882320308, "y"))
    r.append(err_compare(0.0, "y", std_dev=True))
    r.append(err_compare(0.012523489882320315, "z"))
    r.append(err_compare(0.0, "z", std_dev=True))
    n_passed = np.sum(r)
    n_tests = len(r)
    print("{:d} Tests passed out of {:d} Tests.".format(n_passed,n_tests))
    return n_passed == n_tests