code: def _solve_hssp(
      rank_i_loss_vals: np.ndarray,
      rank_i_indices: np.ndarray,
      subset_size: int,
      reference_point: np.ndarray,
  ) -> np.ndarray:
      """Solve a hypervolume subset selection problem (HSSP) via a greedy algorithm.
  
      This method is a 1-1/e approximation algorithm to solve HSSP.
  
      For further information about algorithms to solve HSSP, please refer to the following
      paper:
  
      - `Greedy Hypervolume Subset Selection in Low Dimensions
         <https://ieeexplore.ieee.org/document/7570501>`_
      """
      selected_vecs: List[np.ndarray] = []
      selected_indices: List[int] = []
      contributions = [
          optuna._hypervolume.WFG().compute(
              np.asarray([v]), reference_point
          )
          for v in rank_i_loss_vals
      ]
      hv_selected = 0.0
      while len(selected_indices) < subset_size:
          max_index = int(np.argmax(contributions))
          contributions[max_index] = -1  # mark as selected
          selected_index = rank_i_indices[max_index]
          selected_vec = rank_i_loss_vals[max_index]
          for j, v in enumerate(rank_i_loss_vals):
              if contributions[j] == -1:
                  continue
              p = np.max([selected_vec, v], axis=0)
              contributions[j] -= (
                  optuna._hypervolume.WFG().compute(
                      np.asarray(selected_vecs + [p]), reference_point
                  )
                  - hv_selected
              )
          selected_vecs += [selected_vec]
          selected_indices += [selected_index]
          hv_selected = optuna._hypervolume.WFG().compute(
              np.asarray(selected_vecs), reference_point
          )
  
      return np.asarray(selected_indices, dtype=int)
  
label_desc: Use image node_id 1 for calling a global function with example usage: _solve_hssp(rank_i_loss_vals, rank_i_indices, subset_size, reference_point) and returns: np