<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def _run_epoch( self, item_features, user_features, interactions, sample_weight, num_threads, loss, ):
 """ Run an individual epoch. """


        if loss in ("warp", "bpr", "warp-kos"):
            # The CSR conversion needs to happen before shuffle indices are created.
            # Calling .tocsr may result in a change in the data arrays of the COO matrix,
            positives_lookup = CSRMatrix(
                self._get_positives_lookup_matrix(interactions)
            )

        # Create shuffle indexes.
        shuffle_indices = np.arange(len(interactions.data), dtype=np.int32)
        self.random_state.shuffle(shuffle_indices)

        lightfm_data = self._get_lightfm_data()

        # Call the estimation routines.
        if loss == "warp":
            fit_warp(
                CSRMatrix(item_features),
                CSRMatrix(user_features),
                positives_lookup,
                interactions.row,
                interactions.col,
                interactions.data,
                sample_weight,
                shuffle_indices,
                lightfm_data,
                self.learning_rate,
                self.item_alpha,
                self.user_alpha,
                num_threads,
                self.random_state,
            )
        elif loss == "bpr":
            fit_bpr(
                CSRMatrix(item_features),
                CSRMatrix(user_features),
                positives_lookup,
                interactions.row,
                interactions.col,
                interactions.data,
                sample_weight,
                shuffle_indices,
                lightfm_data,
                self.learning_rate,
                self.item_alpha,
                self.user_alpha,
                num_threads,
                self.random_state,
            )
        elif loss == "warp-kos":
            fit_warp_kos(
                CSRMatrix(item_features),
                CSRMatrix(user_features),
                positives_lookup,
                interactions.row,
                shuffle_indices,
                lightfm_data,
                self.learning_rate,
                self.item_alpha,
                self.user_alpha,
                self.k,
                self.n,
                num_threads,
                self.random_state,
            )
        else:
            fit_logistic(
                CSRMatrix(item_features),
                CSRMatrix(user_features),
                interactions.row,
                interactions.col,
                interactions.data,
                sample_weight,
                shuffle_indices,
                lightfm_data,
                self.learning_rate,
                self.item_alpha,
                self.user_alpha,
                num_threads,
            )