code: def _generate_default_reference_point(
      n_objectives: int, dividing_parameter: int = 3
  ) -> np.ndarray:
      """Generates default reference points which are `uniformly` spread on a hyperplane."""
      reference_points = np.zeros(
          (
              _multi_choose(n_objectives, dividing_parameter),
              n_objectives,
          )
      )
      for i, comb in enumerate(
          itertools.combinations_with_replacement(
              range(n_objectives), dividing_parameter
          )
      ):
          for j in comb:
              reference_points[i, j] += 1.0
      return reference_points
  
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 2 for calling a global function with example usage: _generate_default_reference_point(n_objectives, dividing_parameter) and returns: reference_points