def _parallel_predict_proba(estimators, estimators_features, X, n_classes, combination, estimators_weight):
    """Private function used to compute (proba-)predictions within a job."""
    n_samples = X.shape[0]
    proba = np.zeros((n_samples, n_classes))

    for estimator, features, weight in zip(estimators, estimators_features, estimators_weight):
        proba_estimator = estimator.predict_proba(X[:, features])
        if combination in ['weighted_voting', 'weighted_bmr']:
            proba += proba_estimator * weight
        else:
            proba += proba_estimator

    return proba
Private function used to compute (proba-)predictions within a job.