code: def export_file_multipart():
      pros_hex = h2o.upload_file(
          pyunit_utils.locate("smalldata/prostate/prostate.csv")
      )
      pros_hex[1] = pros_hex[1].asfactor()
      pros_hex[3] = pros_hex[3].asfactor()
      pros_hex[4] = pros_hex[4].asfactor()
      pros_hex[5] = pros_hex[5].asfactor()
      pros_hex[8] = pros_hex[8].asfactor()
  
      p_sid = pros_hex.runif()
      pros_train = pros_hex[p_sid > 0.2, :]
      pros_test = pros_hex[p_sid <= 0.2, :]
  
      glm = H2OGeneralizedLinearEstimator(family="binomial")
      myglm = glm.train(
          x=list(range(2, pros_hex.ncol)),
          y=1,
          training_frame=pros_train,
      )
      mypred = glm.predict(pros_test)
  
      def id_generator(
          size=6, chars=string.ascii_uppercase + string.digits
      ):
          return "".join(random.choice(chars) for _ in range(size))
  
      path = pyunit_utils.locate("results")
      dname = os.path.join(path, id_generator() + "_prediction")
  
      h2o.export_file(mypred, dname, parts=-1)
  
      assert os.path.isdir(dname)
  
      part_files = glob.glob(os.path.join(dname, "part-m-?????"))
      print(part_files)
      py_pred = pd.concat((pd.read_csv(f) for f in part_files))
      print(py_pred.head())
      h_pred = mypred.as_data_frame(True)
      print(h_pred.head())
  
      # Test to check if py_pred & h_pred are identical
      assert_frame_equal(py_pred, h_pred)
  
file_path: h2o-3/h2o-py/tests/testdir_misc/pyunit_export_file_multi.py
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  import string
  import os
  import glob
  import random
  import pandas
  from pandas.util.testing import assert_frame_equal
label_desc: Use image node_id 1 for calling a global function with example usage: export_file_multipart() and returns: str