class_docstr: Multi-objective sampler using the NSGA-II algorithm.
  
  NSGA-II stands for "Nondominated Sorting Genetic Algorithm II",
  which is a well known, fast and elitist multi-objective genetic algorithm.
  
  For further information about NSGA-II, please refer to the following paper:
  
  - `A fast and elitist multiobjective genetic algorithm: NSGA-II
    <https://ieeexplore.ieee.org/document/996017>`_
  
  Args:
      population_size:
          Number of individuals (trials) in a generation.
          ``population_size`` must be greater than or equal to ``crossover.n_parents``.
          For :class:`~optuna.samplers.nsgaii.UNDXCrossover` and
          :class:`~optuna.samplers.nsgaii.SPXCrossover`, ``n_parents=3``, and for the other
          algorithms, ``n_parents=2``.
  
      mutation_prob:
          Probability of mutating each parameter when creating a new individual.
          If :obj:`None` is specified, the value ``1.0 / len(parent_trial.params)`` is used
          where ``parent_trial`` is the parent trial of the target individual.
  
      crossover:
          Crossover to be applied when creating child individuals.
          The available crossovers are listed here:
          https://optuna.readthedocs.io/en/stable/reference/samplers/nsgaii.html.
  
          :class:`~optuna.samplers.nsgaii.UniformCrossover` is always applied to parameters
          sampled from :class:`~optuna.distributions.CategoricalDistribution`, and by
          default for parameters sampled from other distributions unless this argument
          is specified.
  
          For more information on each of the crossover method, please refer to
          specific crossover documentation.
  
      crossover_prob:
          Probability that a crossover (parameters swapping between parents) will occur
          when creating a new individual.
  
      swapping_prob:
          Probability of swapping each parameter of the parents during crossover.
  
      seed:
          Seed for random number generator.
  
      constraints_func:
          An optional function that computes the objective constraints. It must take a
          :class:`~optuna.trial.FrozenTrial` and return the constraints. The return value must
          be a sequence of :obj:`float` s. A value strictly larger than 0 means that a
          constraints is violated. A value equal to or smaller than 0 is considered feasible.
          If ``constraints_func`` returns more than one value for a trial, that trial is
          considered feasible if and only if all values are equal to 0 or smaller.
  
          The ``constraints_func`` will be evaluated after each successful trial.
          The function won't be called when trials fail or they are pruned, but this behavior is
          subject to change in the future releases.
  
          The constraints are handled by the constrained domination. A trial x is said to
          constrained-dominate a trial y, if any of the following conditions is true:
  
          1. Trial x is feasible and trial y is not.
          2. Trial x and y are both infeasible, but trial x has a smaller overall violation.
          3. Trial x and y are feasible and trial x dominates trial y.
  
          .. note::
              Added in v2.5.0 as an experimental feature. The interface may change in newer
              versions without prior notice. See
              https://github.com/optuna/optuna/releases/tag/v2.5.0.
  
      elite_population_selection_strategy:
          The selection strategy for determining the individuals to survive from the current
          population pool. Default to :obj:`None`.
  
          .. note::
              The arguments ``elite_population_selection_strategy`` was added in v3.3.0 as an
              experimental feature. The interface may change in newer versions without prior
              notice.
              See https://github.com/optuna/optuna/releases/tag/v3.3.0.
  
      child_generation_strategy:
          The strategy for generating child parameters from parent trials. Defaults to
          :obj:`None`.
  
          .. note::
              The arguments ``child_generation_strategy`` was added in v3.3.0 as an experimental
              feature. The interface may change in newer versions without prior notice.
              See https://github.com/optuna/optuna/releases/tag/v3.3.0.
  
      after_trial_strategy:
          A set of procedure to be conducted after each trial. Defaults to :obj:`None`.
  
          .. note::
              The arguments ``after_trial_strategy`` was added in v3.3.0 as an experimental
              feature. The interface may change in newer versions without prior notice.
              See https://github.com/optuna/optuna/releases/tag/v3.3.0.
code: def reseed_rng(self) -> None:
      self._random_sampler.reseed_rng()
      self._rng.rng.seed()
  
imports: from __future__ import annotations
  from collections import defaultdict
  from collections.abc import Callable
  from collections.abc import Sequence
  import hashlib
  from typing import Any
  import warnings
  import optuna
  from optuna.distributions import BaseDistribution
  from optuna.exceptions import ExperimentalWarning
  from optuna.samplers._base import BaseSampler
  from optuna.samplers._lazy_random_state import LazyRandomState
  from optuna.samplers._random import RandomSampler
  from optuna.samplers.nsgaii._after_trial_strategy import NSGAIIAfterTrialStrategy
  from optuna.samplers.nsgaii._child_generation_strategy import NSGAIIChildGenerationStrategy
  from optuna.samplers.nsgaii._crossovers._base import BaseCrossover
  from optuna.samplers.nsgaii._crossovers._uniform import UniformCrossover
  from optuna.samplers.nsgaii._elite_population_selection_strategy import NSGAIIElitePopulationSelectionStrategy
  from optuna.search_space import IntersectionSearchSpace
  from optuna.study import Study
  from optuna.trial import FrozenTrial
  from optuna.trial import TrialState
label_desc: Use image node_id 2 for calling the NSGAIISampler obj's underlying member method code with example usage: obj.reseed_rng() without return types