code: def execute():
      env = Environment(
          train_dataset=get_toy_classification_data(),
          results_path="HyperparameterHunterAssets",
          metrics=["roc_auc_score"],
          cv_type=RepeatedStratifiedKFold,
          cv_params=dict(n_splits=3, n_repeats=2, random_state=32),
          do_full_save=do_full_save,
      )
  
      experiment_0 = CVExperiment(
          model_initializer=XGBClassifier,
          model_init_params=dict(subsample=0.01),
      )
      # Pro Tip: By setting XGBoost's subsample ridiculously low, we can get bad scores on purpose
  
      # Upon completion of this Experiment, we see a warning that not all result files will be saved
      # This is because the final score of the Experiment was below our threshold of 0.75
      # Specifically, we skipped saving prediction files (OOF, holdout, test, or in-fold), and the heartbeat file
  
      # What still got saved is the Experiment's: key information, leaderboard position, and description file
      # These are saved to allow us to use the information for future hyperparameter optimization, and detect repeated Experiments
      # Additionally, the Experiment's script backup is saved, but that's because its one of the first things that happens
      # For even finer control over what gets saved, use `do_full_save` together with `file_blacklist`
  
      # Now, lets perform another Experiment that does a bit better than our intentionally miserable one
      experiment_1 = CVExperiment(
          model_initializer=XGBClassifier,
          model_init_params=dict(subsample=0.5),
      )
  
imports: from hyperparameter_hunter import Environment, CVExperiment
  from hyperparameter_hunter.utils.learning_utils import get_toy_classification_data
  from sklearn.model_selection import RepeatedStratifiedKFold
  from xgboost import XGBClassifier
label_desc: Use image node_id 2 for calling a global function with example usage: execute() without return types