class_docstr: Base class for improvement evaluators.
class_docstr_tok: ["Base","class","for","improvement","evaluators","."]
code: def evaluate(
      self,
      trials: List[FrozenTrial],
      study_direction: StudyDirection,
  ) -> float:
      pass
  
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 1 for calling the BaseImprovementEvaluator obj's underlying member method code with example usage: obj.evaluate(trials, study_direction) without return types