code: def test_map_batches_block_bundling_skewed_manual(
      ray_start_regular_shared,
      block_sizes,
      batch_size,
      expected_num_blocks,
  ):
      num_blocks = len(block_sizes)
      ds = ray.data.from_pandas(
          [
              pd.DataFrame({"a": [1] * block_size})
              for block_size in block_sizes
          ]
      )
      # Confirm that we have the expected number of initial blocks.
      assert ds.num_blocks() == num_blocks
      ds = ds.map_batches(
          lambda x: x, batch_size=batch_size
      ).materialize()
  
      # Blocks should be bundled up to the batch size.
      assert ds.num_blocks() == expected_num_blocks
  
imports: import itertools
  import math
  import os
  import signal
  import threading
  import time
  from typing import Iterator
  import numpy
  import pandas
  import pyarrow
  import pyarrow.parquet
  import pytest
  import ray
  from ray._private.test_utils import wait_for_condition
  from ray.data.block import BlockAccessor
  from ray.data.context import DataContext
  from ray.data.tests.conftest import *
  from ray.data.tests.util import column_udf, column_udf_class, extract_values
  from ray.tests.conftest import *
label_desc: Use image node_id 17 for calling a global function with example usage: test_map_batches_block_bundling_skewed_manual(ray_start_regular_shared, block_sizes, batch_size, expected_num_blocks) without return types