def lf_overlaps(L, normalize_by_coverage=False):
    """Return the **fraction of items each LF labels that are also labeled by at
     least one other LF.**

    Note that the maximum possible overlap fraction for an LF is the LF's
    coverage, unless `normalize_by_coverage=True`, in which case it is 1.

    Args:
        L: an n x m scipy.sparse matrix where L_{i,j} is the label given by the
            jth LF to the ith candidate
        normalize_by_coverage: Normalize by coverage of the LF, so that it
            returns the percent of LF labels that have overlaps.
    """
    overlaps = (L != 0).T @ _overlapped_data_points(L) / L.shape[0]
    if normalize_by_coverage:
        overlaps /= lf_coverages(L)
    return np.nan_to_num(overlaps)
Return the **fraction of items each LF labels that are also labeled by at
     least one other LF.**

    Note that the maximum possible overlap fraction for an LF is the LF's
    coverage, unless `normalize_by_coverage=True`, in which case it is 1.

    Args:
        L: an n x m scipy.sparse matrix where L_{i,j} is the label given by the
            jth LF to the ith candidate
        normalize_by_coverage: Normalize by coverage of the LF, so that it
            returns the percent of LF labels that have overlaps.