'Aux. function to make parallel predictions/transformation.'

def _transform(self, X, method):

    self._check_Xy(X)
    method = _check_method(self.base_estimator, method)
    (parallel, p_func, n_jobs) = parallel_func(_gl_transform, self.n_jobs)
    n_jobs = min(n_jobs, X.shape[(-1)])
    y_pred = parallel((p_func(self.estimators_, x_split, method) for x_split in np.array_split(X, n_jobs, axis=(-1))))
    y_pred = np.concatenate(y_pred, axis=2)
    return y_pred
