def rewrite_transitions(self, indices: np.ndarray) -> None:
        """Re-write the goal of some sampled transitions' episodes according to HER.
        Currently applies only HER's 'future' strategy. The new goals will be written \
        directly to the internal batch data temporarily and will be restored right \
        before the next sampling or when using some of the buffer's method (e.g. \
        `add`, `save_hdf5`, etc.). This is to make sure that n-step returns \
        calculation etc., performs correctly without additional alteration.
        """
        if indices.size == 0:
            return

        # Sort indices keeping chronological order
        indices[indices < self._index] += self.maxsize
        indices = np.sort(indices)
        indices[indices >= self.maxsize] -= self.maxsize

        # Construct episode trajectories
        indices = [indices]
        for _ in range(self.horizon - 1):
            indices.append(self.next(indices[-1]))
        indices = np.stack(indices)

        # Calculate future timestep to use
        current = indices[0]
        terminal = indices[-1]
        future_offset = np.random.uniform(size=len(indices[0])) * (terminal - current)
        future_offset = future_offset.astype(int)
        future_t = (current + future_offset)
        ...