code: def test_arrange_OOM():
      """
      PUBDEV-5990 customer reported that h2o.arrange (sorting) takes way more memory than normal for sparse
      datasets of 1G.
  
      Thanks to Lauren DiPerna for finding the dataset to repo the problem.
      """
  
      df = h2o.import_file(
          pyunit_utils.locate("bigdata/laptop/jira/sort_OOM.csv")
      )
      t1 = time.time()
      newFrame = df.sort("sort_col")
      print(newFrame[0, 0])
      elapsed_time = time.time() - t1
      print("time taken to perform sort is {0}".format(elapsed_time))
  
      # check and make sure the sort columns contain the right value after sorting!
      answerFrame = h2o.import_file(
          pyunit_utils.locate("bigdata/laptop/jira/sort_OOM_answer.csv")
      )
  
      # compare sort_col from my sort with answer Frame
      pyunit_utils.compare_frames_local(
          answerFrame["sort_col"], newFrame["sort_col"]
      )
  
      # compare 10 more columns with answer Frame.  Compare all columns will take too long
      allColumns = list(range(0, df.ncols))
      random.shuffle(allColumns)
      pyunit_utils.compare_frames_local(
          answerFrame[allColumns[0:5]], newFrame[allColumns[0:5]]
      )
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  import time
  import random
label_desc: Use image node_id 1 for calling a global function with example usage: test_arrange_OOM() without return types