code: def get_imbalanced_dataset():
      X, y = make_classification(
          n_classes=2,
          class_sep=1.5,
          weights=[0.9, 0.1],
          n_informative=3,
          n_redundant=1,
          flip_y=0,
          n_features=20,
          n_clusters_per_class=1,
          n_samples=1000,
          random_state=10,
      )
      df = pd.DataFrame(X)
      df["target"] = y
      return df
  
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 20 for calling a global function with example usage: get_imbalanced_dataset() and returns: df