human
I wrote the below code

    model = result.model
    n = model.exog.shape[0]
    cov = result.cov_params()
    hess = np.linalg.inv(cov)
    A = (hess / n)
    B = np.linalg.cholesky(A).T
    sigma2 = (np.dot(exog, cov) * exog).sum(1)
    sigma = np.sqrt(sigma2)
    bz = np.linalg.solve(B.T, exog.T).T
    bz /= np.sqrt(n)
    bz /= sigma[:, None]
    bzd = np.diff(bz, 1, axis=0)
    bzdn = (bzd ** 2).sum(1)
    kappa_0 = np.sqrt(bzdn).sum()
    from scipy.stats.distributions import norm
    def func(c):
        return ((((kappa_0 * np.exp(((- (c ** 2)) / 2))) / np.pi) + (2 * (1 - norm.cdf(c)))) - alpha)
    from scipy.optimize import brentq
    (c, rslt) = brentq(func, 1, 10, full_output=True)
    if (rslt.converged == False):
        raise ValueError('Root    finding    error    in    basic    SCR')
    return (sigma, c)

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

def _glm_basic_scr(result, exog, alpha):