code: def generate_recommendation(self):
      self.ranking = []
      for n, train_file in enumerate(self.gb_train_files):
          if self.recommender == "UserKNN":
              rec = UserKNN(
                  train_file=train_file,
                  similarity_metric=self.similarity_metric,
                  as_binary=True,
                  as_similar_first=False,
              )
              rec.compute(verbose=False, verbose_evaluation=False)
              self.ranking += rec.ranking
  
          elif self.recommender == "ItemKNN":
              rec = ItemKNN(
                  train_file=train_file,
                  test_file=self.test_file,
                  similarity_metric=self.similarity_metric,
                  as_binary=True,
              )
              rec.compute(verbose=False, verbose_evaluation=False)
              self.ranking += rec.ranking
  
          elif self.recommender == "MostPopular":
              rec = MostPopular(
                  train_file=train_file,
                  test_file=self.test_file,
                  as_binary=True,
              )
              rec.compute(verbose=False, verbose_evaluation=False)
              self.ranking += rec.ranking
  
          elif self.recommender == "BPRMF":
              rec = BprMF(
                  train_file=train_file,
                  test_file=self.test_file,
                  batch_size=4,
              )
              rec.compute(verbose=False, verbose_evaluation=False)
              self.ranking += rec.ranking
          else:
              raise ValueError(
                  "Error: Recommender not implemented or not exist!"
              )
  
      self.ranking = sorted(self.ranking, key=lambda x: (x[0], -x[2]))
  
imports: from scipy.spatial.distance import squareform, pdist
  import numpy
  import os
  from caserec.clustering.kmedoids import kmedoids
  from caserec.recommenders.item_recommendation.base_item_recommendation import BaseItemRecommendation
  from caserec.recommenders.item_recommendation.itemknn import ItemKNN
  from caserec.recommenders.item_recommendation.most_popular import MostPopular
  from caserec.recommenders.item_recommendation.userknn import UserKNN
  from caserec.utils.process_data import ReadFile, WriteFile
  from caserec.recommenders.item_recommendation.bprmf import BprMF
label_desc: Use image node_id 8 for calling the GroupBasedRecommender obj's underlying member method code with example usage: obj.generate_recommendation() without return types