def METHOD_NAME(fx_module, module, fx_white_list=[]):
    fx_attr = dir(fx_module)
    org_attr = dir(module)
    ignore_match_patterns = [r"_", r"quant", r"dequant", r"weight",
                            r"bias", r'activation_post_process']
    ignore_search_patterns = [r"_scale_", r"_zero_point_",
                            r'_activation_post_process_']
    add_special_patterns = [r"_forward_hooks", r"_forward_pre_hooks", r"_backward_hooks"]
    attr_names = []
    for i in org_attr:
        if type(module) in fx_white_list and type(module) != torch.nn.Sequential \
        and any([re.search(p, i) for p in add_special_patterns]):
            continue
        if any([re.search(p, i) for p in add_special_patterns]) \
        or (i not in fx_attr \
            and not any([re.match(p, i) for p in ignore_match_patterns]) \
            and not any([re.search(p, i) for p in ignore_search_patterns])) :
            attr_names.append(i)
    for name in attr_names:
        attr = getattr(module, name, None)
        if isinstance(attr, torch.nn.Module) or \
        isinstance(attr, torch.quantization.qconfig.QConfig):
            continue
        setattr(fx_module, name, attr)
    return fx_module