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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={}):
 """ Hybrid recommendations simply select half recommendations from the ensemble recommender, and half from the curated one. Duplicate recommendations are accomodated by rank ordering by weight. """


        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)

        ensemble_suggestions = self._ensemble_recommender.recommend(
            client_data, extended_limit, extra_data
        )
        curated_suggestions = self._curated_recommender.recommend(
            client_data, extended_limit, extra_data
        )

        # Generate a set of results from each of the composite
        # recommenders.  We select one item from each recommender
        # sequentially so that we do not bias one recommender over the
        # other.
        merged_results = set()

        while (
            len(merged_results) < limit
            and len(ensemble_suggestions) > 0
            and len(curated_suggestions) > 0
        ):

            r1 = ensemble_suggestions.pop()
            if r1[0] not in [temp[0] for temp in merged_results]:
                merged_results.add(r1)

            # Terminate early if we have an odd number for the limit
            if not (
                len(merged_results) < limit
                and len(ensemble_suggestions) > 0
                and len(curated_suggestions) > 0
            ):
                break

            r2 = curated_suggestions.pop()
            if r2[0] not in [temp[0] for temp in merged_results]:
                merged_results.add(r2)

        if len(merged_results) < limit:
            msg = (
                "Defaulting to empty results. Insufficient recommendations found for client: %s"
                % client_data["client_id"]
            )
            self.logger.info(msg)
            return []

        sorted_results = sorted(
            list(merged_results), key=op.itemgetter(1), reverse=True
        )

        log_data = (client_data["client_id"], str([r[0] for r in sorted_results]))

        self.logger.info(
            "Hybrid recommendations client_id: [%s], guids: [%s]" % log_data
        )
        return sorted_results