code: def model(opt):
      gbr = GradientBoostingRegressor(
          n_estimators=opt["n_estimators"],
          max_depth=opt["max_depth"],
          min_samples_split=opt["min_samples_split"],
      )
  
      c_time = time.time()
      scores = cross_val_score(gbr, X, y, cv=3)
      train_time = time.time() - c_time
  
      cv_score = scores.mean()
  
      # you can create a score that is a composition of two objectives
      score = cv_score / train_time
  
      # instead of just returning the score you can also return the score + a dict
      return score, {"training_time": train_time, "cv_score": cv_score}
  
imports: import time
  from sklearn.model_selection import cross_val_score
  from sklearn.ensemble import GradientBoostingRegressor
  from sklearn.datasets import load_diabetes
  from hyperactive import Hyperactive
label_desc: Use image node_id 1 for calling a global function with example usage: model(opt) and returns: score,  dict