class_docstr: An error evaluator for upper bound on the regret with high-probability confidence.
  
  This evaluator evaluates the regret of current best solution, which defined as the difference
  between the objective value of the best solution and of the global optimum. To be specific,
  this evaluator calculates the upper bound on the regret based on the fact that empirical
  estimator of the objective function is bounded by lower and upper confidence bounds with
  high probability under the Gaussian process model assumption.
  
  Args:
      gp:
          A Gaussian process model on which evaluation base. If not specified, the default
          Gaussian process model is used.
      top_trials_ratio:
          A ratio of top trials to be considered when estimating the regret. Default to 0.5.
      min_n_trials:
          A minimum number of complete trials to estimate the regret. Default to 20.
      min_lcb_n_additional_samples:
          A minimum number of additional samples to estimate the lower confidence bound.
          Default to 2000.
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code: def _validate_input(
      cls,
      trials: List[FrozenTrial],
      search_space: Dict[str, BaseDistribution],
  ) -> None:
      if (
          len([t for t in trials if t.state == TrialState.COMPLETE])
          == 0
      ):
          raise ValueError(
              "Because no trial has been completed yet, the regret bound cannot be evaluated."
          )
  
      if len(search_space) == 0:
          raise ValueError(
              "The intersection search space is empty. This condition is not supported by "
              f"{cls.__name__}."
          )
  
imports: import abc
  from typing import Dict
  from typing import List
  from typing import Optional
  from optuna._experimental import experimental_class
  from optuna.distributions import BaseDistribution
  from optuna.search_space import intersection_search_space
  from optuna.study import StudyDirection
  from optuna.terminator.improvement._preprocessing import AddRandomInputs
  from optuna.terminator.improvement._preprocessing import BasePreprocessing
  from optuna.terminator.improvement._preprocessing import OneToHot
  from optuna.terminator.improvement._preprocessing import PreprocessingPipeline
  from optuna.terminator.improvement._preprocessing import SelectTopTrials
  from optuna.terminator.improvement._preprocessing import ToMinimize
  from optuna.terminator.improvement._preprocessing import UnscaleLog
  from optuna.terminator.improvement.gp.base import _min_lcb
  from optuna.terminator.improvement.gp.base import _min_ucb
  from optuna.terminator.improvement.gp.base import BaseGaussianProcess
  from optuna.terminator.improvement.gp.botorch import _BoTorchGaussianProcess
  from optuna.trial import FrozenTrial
  from optuna.trial import TrialState
label_desc: Use image node_id 4 for calling the RegretBoundEvaluator obj's underlying member method code with example usage: obj._validate_input(cls, trials, search_space) without return types