code: def test_skew_kurt_numeric_only_false(func):
      pytest.importorskip("scipy.stats")
      df = pd.DataFrame(
          {
              "int": [1, 2, 3, 4, 5, 6, 7, 8],
              "float": [1.0, 2.0, 3.0, 4.0, np.nan, 6.0, 7.0, 8.0],
              "dt": [pd.NaT]
              + [datetime(2010, i, 1) for i in range(1, 8)],
          }
      )
      ddf = dd.from_pandas(df, npartitions=2)
  
      ctx = pytest.raises(
          TypeError, match="does not support|does not implement"
      )
  
      with ctx:
          getattr(df, func)(numeric_only=False)
      with ctx:
          getattr(ddf, func)(numeric_only=False)
  
      if PANDAS_GE_150 and not PANDAS_GE_200:
          ctx = pytest.warns(FutureWarning, match="default value")
      elif not PANDAS_GE_150:
          ctx = pytest.warns(FutureWarning, match="nuisance columns")
  
      with ctx:
          getattr(df, func)()
      with ctx:
          getattr(ddf, func)()
  
imports: from __future__ import annotations
  import contextlib
  import warnings
  from datetime import datetime
  import numpy
  import pandas
  import pytest
  from pandas.api.types import is_scalar
  import dask.dataframe
  from dask.array.numpy_compat import _numpy_125
  from dask.dataframe._compat import PANDAS_GE_140, PANDAS_GE_150, PANDAS_GE_200, PANDAS_VERSION, check_numeric_only_deprecation
  from dask.dataframe.utils import assert_dask_graph, assert_eq, make_meta, pyarrow_strings_enabled
label_desc: Use image node_id 22 for calling a global function with example usage: test_skew_kurt_numeric_only_false(func) without return types