def joint_min(mu, var, with_derivatives=False, **kwargs):
    mu = mu[:, 0]
    logP = np.zeros(mu.shape)
    D = mu.shape[0]
    if with_derivatives:
        dlogPdMu = np.zeros((D, D))
        dlogPdSigma = np.zeros((D, 0.5 * D * (D + 1)))
        dlogPdMudMu = np.zeros((D, D, D))
    for i in range(mu.shape[0]):

        # logP[k] ) self._min_faktor(mu, var, 0)
        a = min_faktor(mu, var, i)

        logP[i] = next(a)
        if with_derivatives:
            dlogPdMu[i, :] = a.next().T
            dlogPdMudMu[i, :, :] = next(a)
            dlogPdSigma[i, :] = a.next().T

    logP[np.isinf(logP)] = -500
    # re-normalize at the end, to smooth out numerical imbalances:
    logPold = logP
    Z = np.sum(np.exp(logPold))
    maxLogP = np.max(logP)
    s = maxLogP + np.log(np.sum(np.exp(logP - maxLogP)))
    s = maxLogP if np.isinf(s) else s

    logP = logP - s
    if not with_derivatives:
        return logP

    dlogPdMuold = dlogPdMu
    dlogPdSigmaold = dlogPdSigma
    dlogPdMudMuold = dlogPdMudMu
    # adjust derivatives, too. This is a bit tedious.
    Zm = sum(np.rot90((np.exp(logPold) * np.rot90(dlogPdMuold, 1)), 3)) / Z
    Zs = sum(np.rot90((np.exp(logPold) * np.rot90(dlogPdSigmaold, 1)), 3)) / Z

    dlogPdMu = dlogPdMuold - Zm
    dlogPdSigma = dlogPdSigmaold - Zs

    ff = np.einsum('ki,kj->kij', dlogPdMuold, dlogPdMuold)
    gg = np.einsum('kij,k->ij', dlogPdMudMuold + ff, np.exp(logPold)) / Z
    Zij = Zm.T * Zm
    adds = np.reshape(-gg + Zij, (1, D, D))
    dlogPdMudMu = dlogPdMudMuold + adds
    return logP, dlogPdMu, dlogPdSigma, dlogPdMudMu
Check we can fetch an unannotated tweet, annotate it and store the result
dataset/ETHPy150Open automl/RoBO/robo/util/epmgp.py/joint_min--crossover with--dataset/ETHPy150Open ianozsvald/social_media_brand_disambiguator/ner_apis/test_ner_api_caller.py/Test.test1