code: def _crowding_distance_sort(population: list[FrozenTrial]) -> None:
      manhattan_distances = _calc_crowding_distance(population)
      population.sort(key=lambda x: manhattan_distances[x.number])
      population.reverse()
  
file_path: optuna/optuna/samplers/nsgaii/_elite_population_selection_strategy.py
imports: from __future__ import annotations
  from collections import defaultdict
  from collections.abc import Callable
  from collections.abc import Sequence
  import itertools
  import optuna
  from optuna.samplers.nsgaii._dominates import _constrained_dominates
  from optuna.samplers.nsgaii._dominates import _validate_constraints
  from optuna.study import Study
  from optuna.study._multi_objective import _dominates
  from optuna.trial import FrozenTrial
label_desc: Use image node_id 2 for calling a global function with example usage: _crowding_distance_sort(population) without return types