docstr: Method to predict a rank for each user.
  
  For each pair out of train set, predict a random score for it.
docstr_tok: ["Method","to","predict","a","rank","for","each","user",".","For","each","pair","out","of","train","set",",","predict","a","random","score","for","it","."]
code: def predict(self):
      """
      Method to predict a rank for each user.
  
      For each pair out of train set, predict a random score for it.
  
      """
  
      for user in set(self.users):
          predictions = list()
          for item in self.train_set["items_unobserved"].get(user, []):
              predictions.append((user, item, random.uniform(0, 1)))
          predictions = sorted(predictions, key=lambda x: -x[2])
          self.ranking += predictions[: self.rank_length]
  
file_path: CaseRecommender/caserec/recommenders/item_recommendation/random_rec.py
imports: import random
  from caserec.recommenders.item_recommendation.base_item_recommendation import BaseItemRecommendation
  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