code: def model(opt):
      feature_list = get_feature_list(opt)
      X_new = np.array(feature_list).T
  
      knr = KNeighborsRegressor(n_neighbors=opt["n_neighbors"])
      scores = cross_val_score(knr, X_new, y, cv=5)
      score = scores.mean()
  
      return score
  
file_path: Hyperactive/examples/optimization_applications/feature_transformation.py
imports: import numpy
  import itertools
  from sklearn.datasets import load_diabetes
  from sklearn.model_selection import cross_val_score
  from sklearn.neighbors import KNeighborsRegressor
  from hyperactive import Hyperactive
label_desc: Use image node_id 2 for calling a global function with example usage: model(opt) and returns: score