code: def execute():
      env = Environment(
          train_dataset=get_imbalanced_dataset(),
          results_path="HyperparameterHunterAssets",
          target_column="target",
          metrics=["roc_auc_score", "accuracy_score"],
          cv_type="KFold",
          cv_params=dict(n_splits=5, random_state=7),
      )
  
      # Since this is HyperparameterHunter, after all, we'll throw in some classic hyperparameter
      #   optimization just for fun. If you're like most people and you think it's absurd to test
      #   18 different `imblearn` techniques, feel free to comment out some `EngineerStep`s below
  
      opt_0 = ET(iterations=20, random_state=32)
      opt_0.forge_experiment(
          model_initializer=XGBClassifier,
          model_init_params=dict(
              max_depth=Integer(2, 20),
              n_estimators=Integer(50, 900),
              learning_rate=Real(0.0001, 0.9),
              subsample=0.5,
              booster=Categorical(["gbtree", "gblinear"]),
          ),
          feature_engineer=FeatureEngineer(
              [
                  Categorical(
                      [
                          EngineerStep(
                              resample_smote_tomek, stage="intra_cv"
                          ),
                          EngineerStep(
                              over_sample_random, stage="intra_cv"
                          ),
                          EngineerStep(
                              over_sample_smote, stage="intra_cv"
                          ),
                          EngineerStep(
                              under_sample_random, stage="intra_cv"
                          ),
                          EngineerStep(
                              under_sample_cluster_centroids,
                              stage="intra_cv",
                          ),
                          EngineerStep(
                              under_sample_tomek_links, stage="intra_cv"
                          ),
                          #################### GROUP 2 (EXTENDED) ####################
                          EngineerStep(
                              resample_smote_enn, stage="intra_cv"
                          ),
                          EngineerStep(
                              over_sample_ADASYN, stage="intra_cv"
                          ),
                          EngineerStep(
                              over_sample_BorderlineSMOTE,
                              stage="intra_cv",
                          ),
                          EngineerStep(
                              over_sample_SVMSMOTE, stage="intra_cv"
                          ),
                          EngineerStep(
                              under_sample_NearMiss, stage="intra_cv"
                          ),
                          EngineerStep(
                              under_sample_CondensedNearestNeighbour,
                              stage="intra_cv",
                          ),
                          EngineerStep(
                              under_sample_OneSidedSelection,
                              stage="intra_cv",
                          ),
                          EngineerStep(
                              under_sample_NeighbourhoodCleaningRule,
                              stage="intra_cv",
                          ),
                          EngineerStep(
                              under_sample_EditedNearestNeighbours,
                              stage="intra_cv",
                          ),
                          EngineerStep(
                              under_sample_RepeatedEditedNearestNeighbour,
                              stage="intra_cv",
                          ),
                          EngineerStep(
                              under_sample_AllKNN, stage="intra_cv"
                          ),
                          EngineerStep(
                              under_sample_InstanceHardnessThreshold,
                              stage="intra_cv",
                          ),
                      ],
                      optional=True,
                  )
              ]
          ),
      )
      opt_0.go()
  
imports: from hyperparameter_hunter import Environment, ET, FeatureEngineer, EngineerStep
  from hyperparameter_hunter import Real, Integer, Categorical
  import pandas
  from imblearn.combine import SMOTETomek, SMOTEENN
  from imblearn.over_sampling import RandomOverSampler, SMOTE
  from imblearn.over_sampling import ADASYN, BorderlineSMOTE, SVMSMOTE
  from imblearn.under_sampling import ClusterCentroids, RandomUnderSampler, TomekLinks, NearMiss, CondensedNearestNeighbour, OneSidedSelection, NeighbourhoodCleaningRule, EditedNearestNeighbours, RepeatedEditedNearestNeighbours, AllKNN, InstanceHardnessThreshold
  from sklearn.datasets import make_classification
  from xgboost import XGBClassifier
label_desc: Use image node_id 21 for calling a global function with example usage: execute() without return types