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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _create_base_ensemble(self, out, n_estimators, n_folds):
 """For each base estimator collect models trained on each fold"""

        ensemble_scores = numpy.empty((n_estimators, n_folds))
        base_ensemble = numpy.empty_like(ensemble_scores, dtype=numpy.object)
        for model, fold, score, est in out:
            ensemble_scores[model, fold] = score
            base_ensemble[model, fold] = est

        return ensemble_scores, base_ensemble