code: def report(results, n_top=3):
      ranks = heapq.nlargest(n_top, results["rank_test_score"])
  
      for i in ranks:
          candidates = np.flatnonzero(results["rank_test_score"] == i)
          for candidate in candidates:
              print(
                  "Mean validation score: {0:.3f} (std: {1:.3f})".format(
                      results["mean_test_score"][candidate],
                      results["std_test_score"][candidate],
                  )
              )
              print(
                  "Parameters: {0}".format(results["params"][candidate])
              )
              print("")
  
file_path: neupy/examples/rbfn/grnn_params_selection.py
imports: import heapq
  import numpy
  from sklearn import datasets
  from sklearn.model_selection import RandomizedSearchCV
  from neupy import algorithms
label_desc: Use image node_id 3 for calling a global function with example usage: report(results, n_top) without return types