code: def generate_groups(self):
      fold_for_sets = (
          self.dir_name + "/gb_train_" + str(self.parser) + "/"
      )
      if not os.path.exists(fold_for_sets):
          os.mkdir(fold_for_sets)
  
      train_tuple = self.run_kmedoids()
      self.k_groups = len(train_tuple)
      for f in range(len(train_tuple)):
          train_file_name = fold_for_sets + "train_%d.dat" % f
          WriteFile(
              train_file_name, data=train_tuple[f], sep=self.sep
          ).write()
          self.gb_train_files.append(train_file_name)
      del self.train_set_list
  
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 7 for calling the GroupBasedRecommender obj's underlying member method code with example usage: obj.generate_groups() without return types