code: def __init__(
      self,
      *,
      population_size: int,
      constraints_func: Callable[[FrozenTrial], Sequence[float]]
      | None = None,
      reference_points: np.ndarray | None = None,
      dividing_parameter: int = 3,
      rng: LazyRandomState,
  ) -> None:
      if population_size < 2:
          raise ValueError(
              "`population_size` must be greater than or equal to 2."
          )
  
      self._population_size = population_size
      self._constraints_func = constraints_func
      self._reference_points = reference_points
      self._dividing_parameter = dividing_parameter
      self._rng = rng
  
imports: from __future__ import annotations
  from collections import defaultdict
  from collections.abc import Callable
  from collections.abc import Sequence
  import itertools
  import math
  import numpy
  from optuna.samplers._lazy_random_state import LazyRandomState
  from optuna.samplers.nsgaii._dominates import _constrained_dominates
  from optuna.samplers.nsgaii._dominates import _validate_constraints
  from optuna.samplers.nsgaii._elite_population_selection_strategy import _fast_non_dominated_sort
  from optuna.study import Study
  from optuna.study._multi_objective import _dominates
  from optuna.trial import FrozenTrial
label_desc: Use image node_id 1 to create a new NSGAIIIElitePopulationSelectionStrategy object  with example: obj = NSGAIIIElitePopulationSelectionStrategy()