class_docstr: Sampler using Scikit-Optimize as the backend.
  
  The use of :class:`~optuna.integration.SkoptSampler` is highly not recommended, as the
  development of Scikit-Optimize has been inactive and we have identified compatibility
  issues with newer NumPy versions.
  
  Args:
      independent_sampler:
          A :class:`~optuna.samplers.BaseSampler` instance that is used for independent
          sampling. The parameters not contained in the relative search space are sampled
          by this sampler.
          The search space for :class:`~optuna.integration.SkoptSampler` is determined by
          :func:`~optuna.search_space.intersection_search_space()`.
  
          If :obj:`None` is specified, :class:`~optuna.samplers.RandomSampler` is used
          as the default.
  
          .. seealso::
              :class:`optuna.samplers` module provides built-in independent samplers
              such as :class:`~optuna.samplers.RandomSampler` and
              :class:`~optuna.samplers.TPESampler`.
  
      warn_independent_sampling:
          If this is :obj:`True`, a warning message is emitted when
          the value of a parameter is sampled by using an independent sampler.
  
          Note that the parameters of the first trial in a study are always sampled
          via an independent sampler, so no warning messages are emitted in this case.
  
      skopt_kwargs:
          Keyword arguments passed to the constructor of
          `skopt.Optimizer <https://scikit-optimize.github.io/#skopt.Optimizer>`_
          class.
  
          Note that ``dimensions`` argument in ``skopt_kwargs`` will be ignored
          because it is added by :class:`~optuna.integration.SkoptSampler` automatically.
  
      n_startup_trials:
          The independent sampling is used until the given number of trials finish in the
          same study.
  
      consider_pruned_trials:
          If this is :obj:`True`, the PRUNED trials are considered for sampling.
  
          .. note::
              Added in v2.0.0 as an experimental feature. The interface may change in newer
              versions without prior notice. See
              https://github.com/optuna/optuna/releases/tag/v2.0.0.
  
          .. note::
              As the number of trials :math:`n` increases, each sampling takes longer and longer
              on a scale of :math:`O(n^3)`. And, if this is :obj:`True`, the number of trials
              will increase. So, it is suggested to set this flag :obj:`False` when each
              evaluation of the objective function is relatively faster than each sampling. On
              the other hand, it is suggested to set this flag :obj:`True` when each evaluation
              of the objective function is relatively slower than each sampling.
  
      seed:
          Seed for random number generator.
code: def after_trial(
      self,
      study: Study,
      trial: FrozenTrial,
      state: TrialState,
      values: Optional[Sequence[float]],
  ) -> None:
      self._independent_sampler.after_trial(study, trial, state, values)
  
imports: import copy
  import random
  from typing import Any
  from typing import Dict
  from typing import List
  from typing import Optional
  from typing import Sequence
  from typing import Tuple
  import warnings
  import numpy
  import optuna
  from optuna import distributions
  from optuna import samplers
  from optuna._deprecated import deprecated_class
  from optuna._imports import try_import
  from optuna.exceptions import ExperimentalWarning
  from optuna.samplers import BaseSampler
  from optuna.search_space import IntersectionSearchSpace
  from optuna.study import Study
  from optuna.study._study_direction import StudyDirection
  from optuna.trial import FrozenTrial
  from optuna.trial import TrialState
label_desc: Use image node_id 8 for calling the SkoptSampler obj's underlying member method code with example usage: obj.after_trial(study, trial, state, values) without return types