code: def pubdev_6339():
      cluster = h2o.cluster()
      # number of nodes
      cloud_size = cluster.cloud_size
      # number of CPUs
      cores = sum(node["num_cpus"] for node in cluster.nodes)
  
      # path to file
      file_paths = [
          pyunit_utils.locate("smalldata/arcene/arcene_train.data"),
          pyunit_utils.locate("smalldata/census_income/adult_data.csv"),
          pyunit_utils.locate(
              "smalldata/chicago/chicagoAllWeather.csv"
          ),
          pyunit_utils.locate(
              "smalldata/gbm_test/alphabet_cattest.csv"
          ),
          pyunit_utils.locate(
              "smalldata/wa_cannabis/raw/Dashboard_Usable_Sales_w_Weight_Daily.csv"
          ),
      ]
  
      for file_path in file_paths:
          # read data and parse setup to get number of columns
          data_raw = h2o.import_file(path=file_path, parse=False)
          setup = h2o.parse_setup(data_raw)
  
          # get number of columns from setup
          num_cols = setup["number_columns"]
          # get the chunk size
          chunk_size = calculate_chunk_size(
              file_path, num_cols, cores, cloud_size
          )
  
          # get chunk size to compare if calculation is correct
          result_size = setup["chunk_size"]
          assert (
              chunk_size == result_size
          ), "Calculated chunk size is incorrect!"
          print("chunk size for file", file_path, "is:", chunk_size)
  
      data_raw = h2o.import_file(path=file_paths[1], parse=False)
      setup = h2o.parse_setup(data_raw)
  
imports: import sys
  import h2o
  import math
  import os
  from tests import pyunit_utils
label_desc: Use image node_id 1 for calling a global function with example usage: pubdev_6339() without return types