code: def plot_terminator_improvement(
      study: Study,
      plot_error: bool = False,
      improvement_evaluator: BaseImprovementEvaluator | None = None,
      error_evaluator: BaseErrorEvaluator | None = None,
      min_n_trials: int = DEFAULT_MIN_N_TRIALS,
  ) -> "Axes":
      """Plot the potentials for future objective improvement.
  
      This function visualizes the objective improvement potentials, evaluated
      with ``improvement_evaluator``.
      It helps to determine whether we should continue the optimization or not.
      You can also plot the error evaluated with
      ``error_evaluator`` if the ``plot_error`` argument is set to :obj:`True`.
      Note that this function may take some time to compute
      the improvement potentials.
  
      .. seealso::
          Please refer to :func:`optuna.visualization.plot_terminator_improvement`.
  
      Example:
          The following code snippet shows how to plot improvement potentials,
          together with cross-validation errors.
  
          .. plot::
  
              from lightgbm import LGBMClassifier
              from sklearn.datasets import load_wine
              from sklearn.model_selection import cross_val_score
              from sklearn.model_selection import KFold
              import optuna
              from optuna.terminator import report_cross_validation_scores
              from optuna.visualization.matplotlib import plot_terminator_improvement
  
              def objective(trial):
                  X, y = load_wine(return_X_y=True)
                  clf = LGBMClassifier(
                      reg_alpha=trial.suggest_float("reg_alpha", 1e-8, 10.0, log=True),
                      reg_lambda=trial.suggest_float("reg_lambda", 1e-8, 10.0, log=True),
                      num_leaves=trial.suggest_int("num_leaves", 2, 256),
                      colsample_bytree=trial.suggest_float("colsample_bytree", 0.4, 1.0),
                      subsample=trial.suggest_float("subsample", 0.4, 1.0),
                      subsample_freq=trial.suggest_int("subsample_freq", 1, 7),
                      min_child_samples=trial.suggest_int("min_child_samples", 5, 100),
                  )
                  scores = cross_val_score(clf, X, y, cv=KFold(n_splits=5, shuffle=True))
                  report_cross_validation_scores(trial, scores)
                  return scores.mean()
  
              study = optuna.create_study()
              study.optimize(objective, n_trials=30)
  
              plot_terminator_improvement(study, plot_error=True)
  
      Args:
          study:
              A :class:`~optuna.study.Study` object whose trials are plotted for their improvement.
          plot_error:
              A flag to show the error. If it is set to :obj:`True`, errors
              evaluated by ``error_evaluator`` are also plotted as line graph.
              Defaults to :obj:`False`.
          improvement_evaluator:
              An object that evaluates the improvement of the objective function.
              Default to :class:`~optuna.terminator.RegretBoundEvaluator`.
          error_evaluator:
              An object that evaluates the error inherent in the objective function.
              Default to :class:`~optuna.terminator.CrossValidationErrorEvaluator`.
          min_n_trials:
              The minimum number of trials before termination is considered.
              Terminator improvements for trials below this value are
              shown in a lighter color. Defaults to ``20``.
  
      Returns:
          A :class:`matplotlib.axes.Axes` object.
      """
      _imports.check()
  
      info = _get_improvement_info(
          study, plot_error, improvement_evaluator, error_evaluator
      )
      return _get_improvement_plot(info, min_n_trials)
  
file_path: optuna/optuna/visualization/matplotlib/_terminator_improvement.py
imports: from __future__ import annotations
  from optuna._experimental import experimental_func
  from optuna.logging import get_logger
  from optuna.study.study import Study
  from optuna.terminator import BaseErrorEvaluator
  from optuna.terminator import BaseImprovementEvaluator
  from optuna.terminator.improvement.evaluator import DEFAULT_MIN_N_TRIALS
  from optuna.visualization._terminator_improvement import _get_improvement_info
  from optuna.visualization._terminator_improvement import _get_y_range
  from optuna.visualization._terminator_improvement import _ImprovementInfo
  from optuna.visualization.matplotlib._matplotlib_imports import _imports
label_desc: Use image node_id 1 for calling a global function with example usage: plot_terminator_improvement(study, plot_error, improvement_evaluator, error_evaluator, min_n_trials) and returns: _get_improvement_plot