code: def compute_test_results(
      model, train, test, rating_metrics, ranking_metrics, k=DEFAULT_K
  ):
      """Compute the test results using a trained NCF model.
  
      Args:
          model (object): TF model.
          train (pandas.DataFrame): Train set.
          test (pandas.DataFrame): Test set.
          rating_metrics (list): List of rating metrics.
          ranking_metrics (list): List of ranking metrics.
          k (int): top K recommendations
  
      Returns:
          dict: Test results.
  
      """
      test_results = {}
  
      # Rating Metrics
      predictions = [
          [
              row.userID,
              row.itemID,
              model.predict(row.userID, row.itemID),
          ]
          for (_, row) in test.iterrows()
      ]
  
      predictions = pd.DataFrame(
          predictions, columns=["userID", "itemID", "prediction"]
      )
      predictions = predictions.astype(
          {
              "userID": "int64",
              "itemID": "int64",
              "prediction": "float64",
          }
      )
  
      for metric in rating_metrics:
          test_results[metric] = eval(metric)(test, predictions)
  
      # Ranking Metrics
      users, items, preds = [], [], []
      item = list(train.itemID.unique())
      for user in train.userID.unique():
          user = [user] * len(item)
          users.extend(user)
          items.extend(item)
          preds.extend(list(model.predict(user, item, is_list=True)))
  
      all_predictions = pd.DataFrame(
          data={"userID": users, "itemID": items, "prediction": preds}
      )
  
      merged = pd.merge(
          train, all_predictions, on=["userID", "itemID"], how="outer"
      )
      all_predictions = merged[merged.rating.isnull()].drop(
          "rating", axis=1
      )
  
      for metric in ranking_metrics:
          test_results[metric] = eval(metric)(
              test, all_predictions, col_prediction="prediction", k=k
          )
  
      return test_results
  
label_desc: Use image node_id 1 for calling a global function with example usage: compute_test_results(model, train, test, rating_metrics, ranking_metrics, k) and returns: test_results