code: def compute(
      self,
      verbose=True,
      metrics=None,
      verbose_evaluation=True,
      as_table=False,
      table_sep="\t",
  ):
      if verbose:
          super(MatrixFactorization, self).compute(verbose=verbose)
          self.init_model()
  
          print("training_time:: %4f sec" % timed(self.fit))
          if self.extra_info_header is not None:
              print(self.extra_info_header)
  
          search_time = timed(self.search_new_items)
          replace_time = timed(self.replace_vector_new_item)
          prediction_time = timed(self.predict)
  
          print("search_new_items_time:: %4f sec" % search_time)
          print("vectors_replacement_time:: %4f sec" % replace_time)
          print("prediction_time:: %4f sec" % prediction_time)
          print(
              "total_prediction_time:: %4f sec"
              % (search_time + replace_time + prediction_time)
          )
          print("\n")
  
      else:
          # Execute all in silence without prints
          super(MatrixFactorization, self).compute(verbose=verbose)
          self.init_model()
          self.fit()
          self.search_new_items()
          self.replace_vector_new_item()
          self.predict()
  
      self.write_predictions()
  
      if self.test_file is not None:
          return self.evaluate(
              metrics,
              verbose_evaluation,
              as_table=as_table,
              table_sep=table_sep,
          )
  
file_path: CaseRecommender/caserec/recommenders/rating_prediction/item_msmf.py
imports: import numpy
  from caserec.recommenders.rating_prediction.matrixfactorization import MatrixFactorization
  from caserec.utils.extra_functions import timed
label_desc: Use image node_id 7 for calling the ItemMSMF obj's underlying member method code with example usage: obj.compute(verbose, metrics, verbose_evaluation, as_table, table_sep) and returns: self