code: def test_categorical_non_groupers(
      education_df,
      as_index,
      observed,
      normalize,
      name,
      expected_data,
      request,
  ):
      # GH#46357 Test non-observed categories are included in the result,
      # regardless of `observed`
  
      if Version(np.__version__) >= Version("1.25"):
          request.applymarker(
              pytest.mark.xfail(
                  reason=(
                      "pandas default unstable sorting of duplicates"
                      "issue with numpy>=1.25 with AVX instructions"
                  ),
                  strict=False,
              )
          )
  
      education_df = education_df.copy()
      education_df["gender"] = education_df["gender"].astype("category")
      education_df["education"] = education_df["education"].astype(
          "category"
      )
  
      gp = education_df.groupby(
          "country", as_index=as_index, observed=observed
      )
      result = gp.value_counts(normalize=normalize)
  
      expected_index = [
          ("FR", "male", "low"),
          ("FR", "female", "high"),
          ("FR", "male", "medium"),
          ("FR", "female", "low"),
          ("FR", "female", "medium"),
          ("FR", "male", "high"),
          ("US", "female", "high"),
          ("US", "male", "low"),
          ("US", "female", "low"),
          ("US", "female", "medium"),
          ("US", "male", "high"),
          ("US", "male", "medium"),
      ]
      expected_series = Series(
          data=expected_data,
          index=MultiIndex.from_tuples(
              expected_index,
              names=["country", "gender", "education"],
          ),
          name=name,
      )
      for i in range(1, 3):
          expected_series.index = expected_series.index.set_levels(
              CategoricalIndex(expected_series.index.levels[i]), level=i
          )
  
      if as_index:
          tm.assert_series_equal(result, expected_series)
      else:
          expected = expected_series.reset_index(
              name="proportion" if normalize else "count"
          )
          tm.assert_frame_equal(result, expected)
  
imports: import numpy
  import pytest
  import pandas.util._test_decorators
  from pandas import Categorical, CategoricalIndex, DataFrame, Grouper, Index, MultiIndex, Series, date_range, to_datetime
  import pandas._testing
  from pandas.util.version import Version
label_desc: Use image node_id 24 for calling a global function with example usage: test_categorical_non_groupers(education_df, as_index, observed, normalize, name, expected_data, request) without return types