class_docstr: Base class for multi-objective samplers.
  
  The abstract methods of this class are the same as ones defined by
  :class:`~optuna.samplers.BaseSampler` except for taking
  multi-objective versions of study and trial instances as the arguments.
docstr: Reseed sampler's random number generator.
  
  This method is called by the :class:`~optuna.multi_objective.study.MultiObjectiveStudy`
  instance if trials are executed in parallel with the option ``n_jobs>1``. In that case, the
  sampler instance will be replicated including the state of the random number generator, and
  they may suggest the same values. To prevent this issue, this method assigns a different
  seed to each random number generator.
code: def reseed_rng(self) -> None:
      """Reseed sampler's random number generator.
  
      This method is called by the :class:`~optuna.multi_objective.study.MultiObjectiveStudy`
      instance if trials are executed in parallel with the option ``n_jobs>1``. In that case, the
      sampler instance will be replicated including the state of the random number generator, and
      they may suggest the same values. To prevent this issue, this method assigns a different
      seed to each random number generator.
      """
  
      pass
  
file_path: optuna/optuna/multi_objective/samplers/_base.py
imports: import abc
  from typing import Any
  from typing import Dict
  from optuna import multi_objective
  from optuna._deprecated import deprecated_class
  from optuna.distributions import BaseDistribution
label_desc: Use image node_id 4 for calling the BaseMultiObjectiveSampler obj's underlying member method code with example usage: obj.reseed_rng() without return types