code: def test_singlets_holtwinters(seasonal, h, datatype):
      global airpassengers
      airpassengers = np.asarray(airpassengers, dtype=datatype)
      train = airpassengers[:-h]
      test = airpassengers[-h:]
  
      if seasonal == "multiplicative":
          pytest.xfail(
              "Statsmodels nan errors with gcc 9.3 (Issue #3384)"
          )
  
      sm_hw = sm_ES(
          train,
          initialization_method="heuristic",
          seasonal=seasonal,
          seasonal_periods=12,
      )
      sm_hw = sm_hw.fit()
  
      # train = cudf.Series(train)
  
      cu_hw = cuml_ES(train, seasonal=seasonal, seasonal_periods=12)
      cu_hw.fit()
  
      cu_pred = cu_hw.forecast(h)
      sm_pred = sm_hw.forecast(h)
      cu_r2 = r2_score(cu_pred.to_numpy(), test)
      sm_r2 = r2_score(sm_pred, test)
  
      assert (cu_r2 >= sm_r2) or (abs(cu_r2 - sm_r2) < 2e-1)
  
file_path: cuml/python/cuml/tests/test_holtwinters.py
imports: from sklearn.metrics import r2_score
  from statsmodels.tsa.holtwinters import ExponentialSmoothing
  from cuml.tsa.holtwinters import ExponentialSmoothing
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 1 for calling a global function with example usage: test_singlets_holtwinters(seasonal, h, datatype) without return types