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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 _recommend(self, client_data, limit, extra_data={}):
 """ Ensemble recommendations are aggregated from individual recommenders. The ensemble recommender applies a weight to the recommendation outputs of each recommender to reorder the recommendations to be a better fit. The intuitive understanding is that the total space of recommended addons across all recommenders will include the 'true' addons that should be recommended better than any individual recommender. The ensemble method simply needs to weight each recommender appropriate so that the ordering is correct. """

        self.logger.info("Ensemble recommend invoked")
        preinstalled_addon_ids = client_data.get("installed_addons", [])

        # Compute an extended limit by adding the length of
        # the list of any preinstalled addons.
        extended_limit = limit + len(preinstalled_addon_ids)

        flattened_results = []
        ensemble_weights = self._weight_cache.getWeights()

        for rkey in self.RECOMMENDER_KEYS:
            recommender = self._recommender_map[rkey]

            if recommender.can_recommend(client_data):
                raw_results = recommender.recommend(
                    client_data, extended_limit, extra_data
                )
                reweighted_results = []
                for guid, weight in raw_results:
                    item = (guid, weight * ensemble_weights[rkey])
                    reweighted_results.append(item)
                flattened_results.extend(reweighted_results)

        # Sort the results by the GUID
        flattened_results.sort(key=lambda item: item[0])

        # group by the guid, sum up the weights for recurring GUID
        # suggestions across all recommenders
        guid_grouper = itertools.groupby(flattened_results, lambda item: item[0])

        ensemble_suggestions = []
        for (guid, guid_group) in guid_grouper:
            weight_sum = sum([v for (g, v) in guid_group])
            item = (guid, weight_sum)
            ensemble_suggestions.append(item)

        # Sort in reverse order (greatest weight to least)
        ensemble_suggestions.sort(key=lambda x: -x[1])

        filtered_ensemble_suggestions = [
            (guid, weight)
            for (guid, weight) in ensemble_suggestions
            if guid not in preinstalled_addon_ids
        ]

        results = filtered_ensemble_suggestions[:limit]

        log_data = (
            client_data["client_id"],
            str(ensemble_weights),
            str([r[0] for r in results]),
        )
        self.logger.info(
            "client_id: [%s], ensemble_weight: [%s], guids: [%s]" % log_data
        )
        return results