code: def test_map_batches_block_bundling_auto(
      ray_start_regular_shared, block_size, batch_size
  ):
      # Ensure that we test at least 2 batches worth of blocks.
      num_blocks = max(10, 2 * batch_size // block_size)
      ds = ray.data.range(
          num_blocks * block_size, parallelism=num_blocks
      )
      # Confirm that we have the expected number of initial blocks.
      assert ds.num_blocks() == num_blocks
  
      # Blocks should be bundled up to the batch size.
      ds1 = ds.map_batches(
          lambda x: x, batch_size=batch_size
      ).materialize()
      assert ds1.num_blocks() == math.ceil(
          num_blocks / max(batch_size // block_size, 1)
      )
  
      # Blocks should not be bundled up when batch_size is not specified.
      ds2 = ds.map_batches(lambda x: x).materialize()
      assert ds2.num_blocks() == 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 16 for calling a global function with example usage: test_map_batches_block_bundling_auto(ray_start_regular_shared, block_size, batch_size) without return types