code: def run_kmedoids(self):
      set_train_tuple = []
      support_matrix, clusters = kmedoids(
          self.distance_matrix,
          self.k_groups,
          max_interactions=self.max_int_kmedoids,
          random_seed=123,
      )
  
      for c, cluster in enumerate(clusters.values()):
          self.k_users_in_cluster.append(len(cluster))
          train_tuple = set()
          for user_id in cluster:
              user = self.user_id_to_user[user_id]
              for tr in self.train_set_list:
                  for item in tr["feedback"].get(user, []):
                      train_tuple.add((user, item, 1))
          train_tuple = sorted(
              list(train_tuple), key=lambda x: (x[0], x[1])
          )
          if len(train_tuple) != 0:
              set_train_tuple.append(train_tuple)
  
      return set_train_tuple
  
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 6 for calling the GroupBasedRecommender obj's underlying member method code with example usage: obj.run_kmedoids() and returns: set_train_tuple