class_docstr: Base class for samplers.
  
  Optuna combines two types of sampling strategies, which are called *relative sampling* and
  *independent sampling*.
  
  *The relative sampling* determines values of multiple parameters simultaneously so that
  sampling algorithms can use relationship between parameters (e.g., correlation).
  Target parameters of the relative sampling are described in a relative search space, which
  is determined by :func:`~optuna.samplers.BaseSampler.infer_relative_search_space`.
  
  *The independent sampling* determines a value of a single parameter without considering any
  relationship between parameters. Target parameters of the independent sampling are the
  parameters not described in the relative search space.
  
  More specifically, parameters are sampled by the following procedure.
  At the beginning of a trial, :meth:`~optuna.samplers.BaseSampler.infer_relative_search_space`
  is called to determine the relative search space for the trial.
  During the execution of the objective function,
  :meth:`~optuna.samplers.BaseSampler.sample_relative` is called only once
  when sampling the parameters belonging to the relative search space for the first time.
  :meth:`~optuna.samplers.BaseSampler.sample_independent` is used to sample
  parameters that don't belong to the relative search space.
  
  The following figure depicts the lifetime of a trial and how the above three methods are
  called in the trial.
  
  .. image:: ../../../../image/sampling-sequence.png
  
  |
docstr: Reseed sampler's random number generator.
  
  This method is called by the :class:`~optuna.study.Study` 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.
docstr_tok: ["Reseed","sampler","'s","random","number","generator",".","This","method","is","called","by","the",":","class",":","`","~optuna.study.Study","`","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.study.Study` 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
  
imports: import abc
  from typing import Any
  from typing import Callable
  from typing import Dict
  from typing import Optional
  from typing import Sequence
  import warnings
  import numpy
  from optuna.distributions import BaseDistribution
  from optuna.study import Study
  from optuna.trial import FrozenTrial
  from optuna.trial import TrialState
label_desc: Use image node_id 7 for calling the BaseSampler obj's underlying member method code with example usage: obj.reseed_rng() without return types