code: def test_groupby_map_groups_multicolumn(
      ray_start_regular_shared,
      ds_format,
      num_parts,
      use_push_based_shuffle,
  ):
      # Test built-in count aggregation
      print(
          f"Seeding RNG for test_groupby_arrow_count with: {RANDOM_SEED}"
      )
      random.seed(RANDOM_SEED)
      xs = list(range(100))
      random.shuffle(xs)
  
      ds = ray.data.from_items(
          [{"A": (x % 2), "B": (x % 3)} for x in xs]
      ).repartition(num_parts)
  
      agg_ds = ds.groupby(["A", "B"]).map_groups(
          lambda df: {"count": [len(df["A"])]}, batch_format=ds_format
      )
      assert agg_ds.count() == 6
      assert agg_ds.take_all() == [
          {"count": 17},
          {"count": 16},
          {"count": 17},
          {"count": 17},
          {"count": 17},
          {"count": 16},
      ]
  
imports: import itertools
  import math
  import random
  import time
  from unittest.mock import patch
  import numpy
  import pandas
  import pyarrow
  import pytest
  import ray
  from ray.data.aggregate import AggregateFn, Count, Max, Mean, Min, Quantile, Std, Sum
  from ray.data.context import DataContext
  from ray.data.tests.conftest import *
  from ray.data.tests.util import column_udf, named_values
  from ray.tests.conftest import *
label_desc: Use image node_id 36 for calling a global function with example usage: test_groupby_map_groups_multicolumn(ray_start_regular_shared, ds_format, num_parts, use_push_based_shuffle) without return types