code: def test_CFFilter_wrapper():
      """Verify that the wrapped CFFilter estimator agrees with statsmodel."""
      # moved all potential soft dependency import inside the test:
      import statsmodels.api as sm
  
      from sktime.transformations.series.cffilter import (
          CFFilter as _CFFilter,
      )
  
      dta = sm.datasets.macrodata.load_pandas().data
      index = pd.date_range(start="1959Q1", end="2009Q4", freq="Q")
      dta.set_index(index, inplace=True)
      sm_cycles = sm.tsa.filters.cffilter(
          dta[["realinv"]], 6, 32, True
      )[0]
      cf = _CFFilter(6, 32, True)
      sk_cycles = cf.fit_transform(X=dta[["realinv"]]).squeeze(
          "columns"
      )
      assert array_equal(sm_cycles, sk_cycles)
  
imports: import pandas
  import pytest
  from numpy import array_equal
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 1 for calling a global function with example usage: test_CFFilter_wrapper() without return types