human
Solve the following python programming problem given the following code:
def _glm_basic_scr(result, exog, alpha) : 
    "
    The basic SCR from (Sun et al. Annals of Statistics 2000).

    Computes simultaneous confidence regions (SCR).

    Parameters
    ----------
    result : results instance
        The fitted GLM results instance
    exog : array-like
        The exog values spanning the interval
    alpha : float
        `1 - alpha` is the coverage probability.

    Returns
    -------
    An array with two columns, containing the lower and upper
    confidence bounds, respectively.

    Notes
    -----
    The rows of `exog` should be a sequence of covariate values
    obtained by taking one 'free variable' x and varying it over an
    interval.  The matrix `exog` is thus the basis functions and any
    other covariates evaluated as x varies.
    " 
    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 (rslt) 
    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) 

    
    

    Fix the buggy line: sigma = np.sqrt (rslt)
assistant
#Here is Python code to fix the buggy line: sigma = np.

sigma = np.sqrt (sigma2)