code: def test_groupby_skew_equivalence():
      # Test that that groupby skew method (which uses libgroupby.group_skew)
      #  matches the results of operating group-by-group (which uses nanops.nanskew)
      nrows = 1000
      ngroups = 3
      ncols = 2
      nan_frac = 0.05
  
      arr = np.random.default_rng(2).standard_normal((nrows, ncols))
      arr[np.random.default_rng(2).random(nrows) < nan_frac] = np.nan
  
      df = pd.DataFrame(arr)
      grps = np.random.default_rng(2).integers(0, ngroups, size=nrows)
      gb = df.groupby(grps)
  
      result = gb.skew()
  
      grpwise = [grp.skew().to_frame(i).T for i, grp in gb]
      expected = pd.concat(grpwise, axis=0)
      expected.index = expected.index.astype(
          result.index.dtype
      )  # 32bit builds
      tm.assert_frame_equal(result, expected)
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_groupby_skew_equivalence() without return types