def METHOD_NAME(model, deploy_model, save_path=None):
    """
    Examples:
        >>> train_model = RepVGG(arch='A0', deploy=False)
        >>> deploy_model = RepVGG(arch='A0', deploy=True)
        >>> deploy_model = repvgg_model_convert(
        >>>     train_model, deploy_model, save_path='repvgg_deploy.pth')
    """
    converted_weights = {}
    for name, module in model.named_modules():
        if hasattr(module, "repvgg_convert"):
            kernel, bias = module.repvgg_convert()
            converted_weights[name + ".rbr_reparam.weight"] = kernel
            converted_weights[name + ".rbr_reparam.bias"] = bias
        elif isinstance(module, torch.nn.Linear):
            converted_weights[name + ".weight"] = module.weight.detach().cpu().numpy()
            converted_weights[name + ".bias"] = module.bias.detach().cpu().numpy()
    del model
    for name, param in deploy_model.named_parameters():
        print("deploy param: ", name, param.size(), np.mean(converted_weights[name]))
        param.data = torch.from_numpy(converted_weights[name]).float()
    if save_path is not None:
        torch.save(deploy_model.state_dict(), save_path)
    return deploy_model