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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def check_opt(opt, loop, ht, htarg, verb):
 r"""Check optimization parameters. This check-function is called from one of the modelling routines in :mod:`model`. Consult these modelling routines for a detailed description of the input parameters. Parameters opt : {None, 'parallel'} Optimization flag; use ``numexpr`` or not. loop : {None, 'freq', 'off'} Loop flag. ht : str Flag to choose the Hankel transform. htarg : array_like, Depends on the value for ``ht``. verb : {0, 1, 2, 3, 4} Level of verbosity. Returns ------- use_ne_eval : bool Boolean if to use ``numexpr``. loop_freq : bool Boolean if to loop over frequencies. loop_off : bool Boolean if to loop over offsets. """


    # Check optimization flag
    use_ne_eval = False
    if opt == 'parallel':
        if numexpr:
            use_ne_eval = numexpr.evaluate
        elif verb > 0:
            print(numexpr_msg)

    # Define if to loop over frequencies or over offsets
    lagged_splined_fht = False
    if ht == 'fht':
        if htarg[1] != 0:
            lagged_splined_fht = True

    if ht in ['hqwe', 'hquad'] or lagged_splined_fht:
        loop_freq = True
        loop_off = False
    else:
        loop_off = loop == 'off'
        loop_freq = loop == 'freq'

    # If verbose, print optimization information
    if verb > 2:
        if use_ne_eval:
            print("   Kernel Opt.     :  Use parallel")
        else:
            print("   Kernel Opt.     :  None")

        if loop_off:
            print("   Loop over       :  Offsets")
        elif loop_freq:
            print("   Loop over       :  Frequencies")
        else:
            print("   Loop over       :  None (all vectorized)")

    return use_ne_eval, loop_freq, loop_off