code: def model(opt):
      cbc = CatBoostClassifier(
          iterations=10,
          depth=opt["depth"],
          learning_rate=opt["learning_rate"],
      )
      scores = cross_val_score(cbc, X, y, cv=3)
  
      return scores.mean()
  
imports: from sklearn.model_selection import cross_val_score
  from catboost import CatBoostClassifier
  from sklearn.datasets import load_breast_cancer
  from hyperactive import Hyperactive
label_desc: Use image node_id 1 for calling a global function with example usage: model(opt) and returns: scores