code: def predict(self):
      if not self.uniform:
          feedbacks = []
          for user in self.train_set["users"]:
              for item in self.train_set["items_seen_by_user"][user]:
                  feedbacks.append(
                      self.train_set["feedback"][user][item]
                  )
  
          std = np.std(feedbacks)
  
      if self.test_file is not None:
          for user in self.test_set["users"]:
              for item in self.test_set["feedback"][user]:
                  if self.uniform:
                      feedback_value = np.random.uniform(
                          self.train_set["min_value"],
                          self.train_set["max_value"],
                      )
                  else:
                      feedback_value = np.random.normal(
                          self.train_set["mean_value"], std
                      )
  
                  self.predictions.append((user, item, feedback_value))
      else:
          raise NotImplemented
  
file_path: CaseRecommender/caserec/recommenders/rating_prediction/random_rec.py
imports: import numpy
  from caserec.recommenders.rating_prediction.base_rating_prediction import BaseRatingPrediction
  from caserec.utils.extra_functions import timed
label_desc: Use image node_id 2 for calling the RandomRec obj's underlying member method code with example usage: obj.predict() without return types