def METHOD_NAME(model_name, func_name):
    """
    Set values and bounds to Model parameters in lmfit.
    Parameters
    ----------
    model_name : class object
        Model class object from lmfit
    func_name : function
        function name of physics peak
    """
    paras = inspect.getargspec(func_name)
    # the first argument is independent variable, also ignored
    # default values are not considered for fitting in this function
    my_args = paras.args[1:]
    para_dict = get_para()
    for name in my_args:
        if name not in para_dict.keys():
            continue
        my_dict = para_dict[name]
        if my_dict["bound_type"] == "none":
            model_name.set_param_hint(name, vary=True)
        elif my_dict["bound_type"] == "fixed":
            model_name.set_param_hint(name, vary=False, value=my_dict["value"])
        elif my_dict["bound_type"] == "lo":
            model_name.set_param_hint(name, value=my_dict["value"], vary=True, min=my_dict["min"])
        elif my_dict["bound_type"] == "hi":
            model_name.set_param_hint(name, value=my_dict["value"], vary=True, max=my_dict["max"])
        elif my_dict["bound_type"] == "lohi":
            model_name.set_param_hint(
                name, value=my_dict["value"], vary=True, min=my_dict["min"], max=my_dict["max"]
            )
        else:
            raise TypeError("Boundary type {0} can't be " "used".format(my_dict["bound_type"]))