def METHOD_NAME():
    """
    A shim-retriever to lazy-retrieve the necessary shims as needed.
    Because pyhf.tensor is a lazy-retriever for the backends, we can be sure
    that tensorlib is imported correctly.
    """
    tensorlib, _ = get_backend()
    if tensorlib.name == 'numpy':
        from pyhf.optimize.opt_numpy import wrap_objective as numpy_shim
        return numpy_shim
    if tensorlib.name == 'tensorflow':
        from pyhf.optimize.opt_tflow import wrap_objective as tflow_shim
        return tflow_shim
    if tensorlib.name == 'pytorch':
        from pyhf.optimize.opt_pytorch import wrap_objective as pytorch_shim
        return pytorch_shim
    if tensorlib.name == 'jax':
        from pyhf.optimize.opt_jax import wrap_objective as jax_shim
        return jax_shim
    raise ValueError(f'No optimizer shim for {tensorlib.name}.')