code: def test_groupby_split_out(split_out, column):
      df = pd.DataFrame(
          {
              "a": np.arange(8),
              "b": [1, 0, 0, 2, 1, 1, 2, 0],
              "c": [0, 1] * 4,
              "d": [
                  "dog",
                  "cat",
                  "cat",
                  "dog",
                  "dog",
                  "dog",
                  "cat",
                  "bird",
              ],
          }
      ).fillna(0)
      df["e"] = df["d"].astype("category")
  
      gdf = cudf.from_pandas(df)
  
      ddf = dd.from_pandas(df, npartitions=3)
      gddf = dask_cudf.from_cudf(gdf, npartitions=3)
  
      ddf_result = (
          ddf.groupby(column)
          .a.mean(split_out=split_out)
          .compute()
          .sort_values()
          .dropna()
      )
      gddf_result = (
          gddf.groupby(column)
          .a.mean(split_out=split_out)
          .compute()
          .sort_values()
      )
  
      dd.assert_eq(gddf_result, ddf_result, check_index=False)
  
imports: import numpy
  import pandas
  import pytest
  import dask
  from dask import dataframe
  from dask.utils_test import hlg_layer
  import cudf
  import dask_cudf
  from dask_cudf.groupby import OPTIMIZED_AGGS, _aggs_optimized
label_desc: Use image node_id 9 for calling a global function with example usage: test_groupby_split_out(split_out, column) without return types