code: def pub_444_spaces_in_filenames():
      # tempdir = "smalldata/jira/"
      # if was okay to write to smalldata, it's okay to write to the current directory
      # probably don't want to, but can't find what the standard temp directory is supposed to be. no sandbox?
      tempdir = "./"
      # make a few files with spaces in the name
      f1 = open(pyunit_utils.locate(tempdir) + "foo .csv", "w")
      f1.write("response, predictor\n")
      for i in range(10):
          f1.write("1, a\n")
          f1.write("0, b\n")
          f1.write("1, a\n" if random.randint(0, 1) else "0, b\n")
      f1.close()
  
      f2 = open(pyunit_utils.locate(tempdir) + "b a r .csv", "w")
      f2.write("response, predictor\n")
      for i in range(10):
          f2.write("1, a\n")
          f2.write("0, b\n")
          f2.write("1, a\n" if random.randint(0, 1) else "0, b\n")
      f2.close()
  
      f3 = open(pyunit_utils.locate(tempdir) + " ba z.csv", "w")
      for i in range(10):
          f3.write("1, a\n")
          f3.write("0, b\n")
          f3.write("1, a\n" if random.randint(0, 1) else "0, b\n")
      f3.close()
  
      train_data = h2o.upload_file(
          path=pyunit_utils.locate(tempdir + "foo .csv")
      )
      train_data.show()
      train_data.describe()
      train_data["response"] = train_data["response"].asfactor()
      gbm = H2OGradientBoostingEstimator(
          ntrees=1, distribution="bernoulli", min_rows=1
      )
      gbm.train(
          x=list(range(1, train_data.ncol)),
          y="response",
          training_frame=train_data,
      )
      gbm.show()
  
      train_data = h2o.upload_file(
          path=pyunit_utils.locate(tempdir + "b a r .csv")
      )
      train_data.show()
      train_data.describe()
      train_data["response"] = train_data["response"].asfactor()
  
      gbm = H2OGradientBoostingEstimator(
          ntrees=1, distribution="bernoulli", min_rows=1
      )
      gbm.train(x=1, y="response", training_frame=train_data)
  
      gbm.show()
  
      train_data = h2o.upload_file(
          path=pyunit_utils.locate(tempdir + " ba z.csv")
      )
      train_data.show()
      train_data.describe()
      train_data[0] = train_data[0].asfactor()
      gbm = H2OGradientBoostingEstimator(
          ntrees=1, distribution="bernoulli", min_rows=1
      )
      gbm.train(x=1, y=0, training_frame=train_data)
      gbm.show()
  
      os.remove(pyunit_utils.locate(tempdir) + "foo .csv")
      os.remove(pyunit_utils.locate(tempdir) + "b a r .csv")
      os.remove(pyunit_utils.locate(tempdir) + " ba z.csv")
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  import os
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
  import random
label_desc: Use image node_id 1 for calling a global function with example usage: pub_444_spaces_in_filenames() without return types