code: def test_polars_datatable_2_pandas():
      file1 = "bigdata/laptop/jira/PUBDEV_5266_merge_with_string_columns/PUBDEV_5266_f1.csv"
      original_converted_frame1 = single_thread_pandas_conversion(
          file1
      )  # need to run conversion in single thread
  
      if can_install_datatable():
          if not (can_use_datatable()):
              pyunit_utils.install("datatable")
          with local_context(
              polars_disabled=True
          ):  # run with datatable
              print("test data frame conversion using datatable.")
              test_frame_conversion(
                  file1, original_converted_frame1, "datatable"
              )
  
      else:
          print(
              "datatable is not available.  Skipping tests using datatable."
          )
  
      if can_install_polars():
          if not (can_use_polars()):
              pyunit_utils.install("polars")
          if not (can_use_pyarrow()):
              pyunit_utils.install("pyarrow")
          with local_context(datatable_disabled=True):
              print(
                  "test data frame conversion using polars and pyarrow."
              )
              test_frame_conversion(
                  file1, original_converted_frame1, "polars and pyarrow"
              )
      else:
          print(
              "polars, pyarrow are not available.  Skipping tests using polars and pyarrow"
          )
  
file_path: h2o-3/h2o-py/tests/testdir_misc/pyunit_gh_15729_15936_datatable_polars_2_pandas_large.py
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.utils.shared_utils import can_use_datatable, can_use_polars, can_use_pyarrow, can_install_datatable, can_install_polars
  import time
  from h2o.utils.threading import local_context
label_desc: Use image node_id 3 for calling a global function with example usage: test_polars_datatable_2_pandas() without return types