docstr: Method to predict a rank for each user.
docstr_tok: ["Method","to","predict","a","rank","for","each","user","."]
code: def predict(self):
      """
      Method to predict a rank for each user.
  
      """
  
      for u_id, user in enumerate(self.users):
          if len(self.train_set["feedback"].get(user, [])) != 0:
              u_list = list(np.flatnonzero(self.matrix[u_id] == 0))
  
              if self.as_similar_first:
                  self.ranking += self.predict_similar_first_scores(
                      user, u_id, u_list
                  )
              else:
                  self.ranking += self.predict_scores(
                      user, u_id, u_list
                  )
          else:
              # Implement cold start user
              pass
  
file_path: CaseRecommender/caserec/recommenders/item_recommendation/userknn.py
imports: import numpy
  from caserec.recommenders.item_recommendation.base_item_recommendation import BaseItemRecommendation
  from caserec.utils.extra_functions import timed
label_desc: Use image node_id 3 for calling the UserKNN obj's underlying member method code with example usage: obj.predict() without return types