code: def test_issue_29():
      data = load_diabetes()
      X, y = data.data, data.target
  
      def model(para):
          gbr = GradientBoostingRegressor(
              n_estimators=para["n_estimators"],
              max_depth=para["max_depth"],
              min_samples_split=para["min_samples_split"],
          )
          scores = cross_val_score(gbr, X, y, cv=3)
  
          print(
              "Iteration:",
              para.optimizer.nth_iter,
              " Best score",
              para.optimizer.best_score,
          )
  
          return scores.mean()
  
      search_space = {
          "n_estimators": list(range(10, 150, 5)),
          "max_depth": list(range(2, 12)),
          "min_samples_split": list(range(2, 22)),
      }
  
      hyper = Hyperactive()
      hyper.add_search(model, search_space, n_iter=20)
      hyper.run()
  
imports: from sklearn.datasets import load_diabetes
  from sklearn.ensemble import GradientBoostingRegressor
  from sklearn.model_selection import cross_val_score
  from hyperactive import Hyperactive
label_desc: Use image node_id 1 for calling a global function with example usage: test_issue_29() and returns: scores