code: def _execute():
      env = Environment(
          train_dataset=get_breast_cancer_data(),
          results_path="HyperparameterHunterAssets",
          target_column="diagnosis",
          metrics=["roc_auc_score"],
          cv_type=StratifiedKFold,
          cv_params=dict(n_splits=10, shuffle=True, random_state=32),
          runs=2,
      )
  
      optimizer = BayesianOptPro(
          iterations=10, read_experiments=True, random_state=None
      )
  
      optimizer.forge_experiment(
          model_initializer=XGBClassifier,
          model_init_params=dict(
              max_depth=Integer(2, 20),
              learning_rate=Real(0.0001, 0.5),
              n_estimators=200,
              subsample=0.5,
              booster=Categorical(["gbtree", "gblinear", "dart"]),
          ),
          model_extra_params=dict(
              fit=dict(eval_metric=Categorical(["auc", "rmse", "mae"]))
          ),
      )
  
      optimizer.go()
  
imports: from hyperparameter_hunter import Environment, Real, Integer, Categorical, BayesianOptPro
  from hyperparameter_hunter.utils.learning_utils import get_breast_cancer_data
  from sklearn.model_selection import StratifiedKFold
  from xgboost import XGBClassifier
label_desc: Use image node_id 1 for calling a global function with example usage: _execute() without return types