code: def test_repartition_on_categoricals(npartitions):
      df = pd.DataFrame({"x": range(10), "y": list("abababcbcb")})
      if pyarrow_strings_enabled():
          # we need this because a CategoricalDtype backed by arrow strings
          # is not the same as CategoricalDtype backed by object strings
          df = to_pyarrow_string(df)
  
      ddf = dd.from_pandas(df, npartitions=2)
      ddf["y"] = ddf["y"].astype("category")
      ddf2 = ddf.repartition(npartitions=npartitions)
  
      df = df.copy()
      df["y"] = df["y"].astype("category")
      assert_eq(df, ddf)
      assert_eq(df, ddf2)
  
imports: from __future__ import annotations
  import contextlib
  import operator
  import warnings
  import numpy
  import pandas
  import pytest
  import dask
  import dask.dataframe
  from dask.dataframe import _compat
  from dask.dataframe._compat import PANDAS_GE_150, PANDAS_GE_200, PANDAS_GE_210, tm
  from dask.dataframe._pyarrow import to_pyarrow_string
  from dask.dataframe.core import _concat
  from dask.dataframe.utils import assert_eq, clear_known_categories, get_string_dtype, make_meta, pyarrow_strings_enabled
label_desc: Use image node_id 8 for calling a global function with example usage: test_repartition_on_categoricals(npartitions) without return types