def METHOD_NAME():
     """
     This function tests if required covariates exist and are sufficiently long
     (i.e., cover forecast start date + forecast horizon)
     """
     # Create Darts Timeseries test covariates
     n = 10
     start_date = '2019-09-05T09:00:00Z'
     cfg =  {
          'horizon': n,
          'fc_start_timestamp': pd.Timestamp(
               isoparse(start_date)).tz_convert('UTC'),
          'frequency': dt.timedelta(hours=1),    
     }
     cov1 = pd.Series(list(range(n)), pd.date_range(start_date, periods=n, freq='H'))
     cov2 = pd.Series(list(range(n)), pd.date_range(start_date, periods=n, freq='H'))
     cov3 = pd.Series(list(range(n)), pd.date_range(start_date, periods=n, freq='H'))
     # create df
     df = pd.DataFrame({'cov1': cov1, 'cov2': cov2, 'cov3': cov3})
     covs = TimeSeries.from_dataframe(df)
     cfg['horizon'] = n 
     assert check_if_enough_covs_exist(cfg, covs) == True
     # fc_start_timestamp is not in covs  
     cfg['horizon'] = n + 1
     with pytest.raises(ValueError):
         check_if_enough_covs_exist(cfg, covs)