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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 predict( self, user_ids, item_ids, item_features=None, user_features=None, num_threads=1 ):
 """ Compute the recommendation score for user-item pairs. For details on how to use feature matrices, see the documentation on the :class:`lightfm.LightFM` class. Arguments --------- user_ids: integer or np.int32 array of shape [n_pairs,] single user id or an array containing the user ids for the user-item pairs for which a prediction is to be computed. Note that these are LightFM's internal id's, i.e. the index of the user in the interaction matrix used for fitting the model. item_ids: np.int32 array of shape [n_pairs,] an array containing the item ids for the user-item pairs for which a prediction is to be computed. Note that these are LightFM's internal id's, i.e. the index of the item in the interaction matrix used for fitting the model. user_features: np.float32 csr_matrix of shape [n_users, n_user_features], optional Each row contains that user's weights over features. item_features: np.float32 csr_matrix of shape [n_items, n_item_features], optional Each row contains that item's weights over features. num_threads: int, optional Number of parallel computation threads to use. Should not be higher than the number of physical cores. Returns ------- np.float32 array of shape [n_pairs,] Numpy array containing the recommendation scores for pairs defined by the inputs. """


        self._check_initialized()

        if not isinstance(user_ids, np.ndarray):
            user_ids = np.repeat(np.int32(user_ids), len(item_ids))

        if isinstance(item_ids, (list, tuple)):
            item_ids = np.array(item_ids, dtype=np.int32)

        assert len(user_ids) == len(item_ids)

        if user_ids.dtype != np.int32:
            user_ids = user_ids.astype(np.int32)
        if item_ids.dtype != np.int32:
            item_ids = item_ids.astype(np.int32)

        if num_threads < 1:
            raise ValueError("Number of threads must be 1 or larger.")

        if user_ids.min() < 0 or item_ids.min() < 0:
            raise ValueError(
                "User or item ids cannot be negative. "
                "Check your inputs for negative numbers "
                "or very large numbers that can overflow."
            )

        n_users = user_ids.max() + 1
        n_items = item_ids.max() + 1

        (user_features, item_features) = self._construct_feature_matrices(
            n_users, n_items, user_features, item_features
        )

        lightfm_data = self._get_lightfm_data()

        predictions = np.empty(len(user_ids), dtype=np.float64)

        predict_lightfm(
            CSRMatrix(item_features),
            CSRMatrix(user_features),
            user_ids,
            item_ids,
            predictions,
            lightfm_data,
            num_threads,
        )

        return predictions