def test_gae():
 generate('shifts')
 yaml_file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../gae_demo'))
 save_path = os.path.dirname(os.path.realpath(__file__))
 train(yaml_file_path, save_path)
 try:
  os.remove('{0}/train_preprocessed.pkl'.format(save_path))
  os.remove('{0}/gae_196_50.pkl'.format(save_path))
  os.remove('{0}/gae_196_50_best.pkl'.format(save_path))
 except OSError:
  logging.warning('Files not found')

def test_gae ( ) : generate ( 'shifts' ) yaml_file_path = os . path . abspath ( os . path . join ( os . path . dirname ( __file__ ) , '../gae_demo' ) ) save_path = os . path . dirname ( os . path . realpath ( __file__ ) ) train ( yaml_file_path , save_path ) try : os . remove ( '{0}/train_preprocessed.pkl' . format ( save_path ) ) os . remove ( '{0}/gae_196_50.pkl' . format ( save_path ) ) os . remove ( '{0}/gae_196_50_best.pkl' . format ( save_path ) ) except OSError : logging . warning ( 'Files not found' )
the function generates a dataset and uses it to train the model .