def _get_warped_variance(self, mean, std, pred_init=None, deg_gauss_hermite=<NUM_LIT:20>):
gh_samples, gh_weights = np.polynomial.hermite.hermgauss(deg_gauss_hermite)<EOL>gh_samples = gh_samples[:, None]<EOL>gh_weights = gh_weights[None, :]<EOL>arg1 = gh_weights.dot(self._get_warped_term(mean, std, gh_samples, <EOL>pred_init=pred_init) ** <NUM_LIT:2>) / np.sqrt(np.pi)<EOL>arg2 = self._get_warped_mean(mean, std, pred_init=pred_init,<EOL>deg_gauss_hermite=deg_gauss_hermite)<EOL>return arg1 - (arg2 ** <NUM_LIT:2>)<EOL>
Calculate the warped variance by using Gauss-Hermite quadrature.