code: def model(opt):
      gbr = DecisionTreeRegressor(
          max_depth=opt["max_depth"],
          min_samples_split=opt["min_samples_split"],
      )
      scores = cross_val_score(gbr, X, y, cv=10)
  
      return scores.mean()
  
imports: import time
  from sklearn.model_selection import cross_val_score
  from sklearn.tree import DecisionTreeRegressor
  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: scores