docs: def calculate_measures(y_pred, y_test):
      """
      Calculates the F-scores and F-score averages given a classification result and a ground truth.
  
      Inputs:  - y_pred: The node-label prediction for the test set in a SciPy sparse CSR matrix format.
               - y_test: The node-label ground truth for the test set in a SciPy sparse CSR matrix format.
  
      Outputs: - measures: A number of NumPy arrays containing evaluation scores for the experiment.
      """
      y_pred = y_pred.toarray()
      y_test = y_test.toarray()
  
      macro_precision, macro_recall, macro_F1, macro_support = precision_recall_fscore_support(y_test,
                                                                                               y_pred,
                                                                                               beta=1.0,
                                                                                               average="macro")
  
      micro_precision, micro_recall, micro_F1, micro_support = precision_recall_fscore_support(y_test,
                                                                                               y_pred,
                                                                                               beta=1.0,
                                                                                               average="micro")
  
      F1 = f1_score(y_test,
                    y_pred,
                    average=None)
  
      measures = [macro_recall, micro_recall, macro_precision, micro_precision, macro_F1, micro_F1, F1]
      return measures