def METHOD_NAME(x1: list, x2: list, tol: float = 5e-16) -> float:
    """Computes the CorrIndex from the L2-normalized A matrices.
    Parameters
    ----------
    x1 : list
        A list containing normalized A matrix(ces) from the first tensor decomposition.
    x2 : list
        A list containing normalized A matrix(ces) from the first tensor decomposition.
    tol : float, optional
        Precision threshold below which to call the CorrIndex score 0, by default 5e-16
    Returns
    -------
    score : float
         CorrIndex metric [0,1]; lower score indicates higher similarity between matrices
    """
    # generate the correlation index input
    c_prod_mtx = tl.abs(tl.matmul(tl.conj(tl.transpose(x1)), x2))
    # correlation index scoring
    n_elements = tl.shape(c_prod_mtx)[1] + tl.shape(c_prod_mtx)[0]
    score = (1 / (n_elements)) * (
        tl.sum(tl.abs(tl.max(c_prod_mtx, 1) - 1))
        + tl.sum(tl.abs(tl.max(c_prod_mtx, 0) - 1))
    )
    if score < tol:
        score = 0
    return score