human
I wrote the below code

    (m, n) = cov.shape
    (Q, Lambda, _) = svds(cov, rank)
    if sparse.issparse(cov):
        QSQ = np.dot(Q.T, cov.dot(Q))
        ts = cov.diagonal().sum()
        tss = cov.dot(cov).diagonal().sum()
    else:
        QSQ = np.dot(Q.T, np.dot(cov, Q))
        ts = np.trace(cov)
        tss = np.trace(np.dot(cov, cov))
    def fun(k):
        Lambda_t = (Lambda - k)
        v = (((tss + (m * (k ** 2))) + np.sum((Lambda_t ** 2))) - ((2 * k) * ts))
        v += (((2 * k) * np.sum(Lambda_t)) - (2 * np.sum((np.diag(QSQ) * Lambda_t))))
        return v
    k_opt = fminbound(fun, 0, 100000.0)
    Lambda_opt = (Lambda - k_opt)
    fac_opt = (Q * np.sqrt(Lambda_opt))
    diag = (k_opt * np.ones(m, dtype=np.float64))
    return FactoredPSDMatrix(diag, fac_opt)

What's a good function header?
 Here's a good function header?
assistant
#Here's a good function header.

def cov_nearest_factor_homog(cov, rank):