code: def check_warnings(model_id, X, Y, warnNumber, training_data):
      buffer = StringIO()  # redirect output
      sys.stderr = buffer
      model = H2OGeneralizedLinearEstimator(
          family="Gaussian",
          nfolds=2,
          fold_assignment="Modulo",
          keep_cross_validation_predictions=True,
          model_id=model_id,
      )
      model.train(x=X, y=Y, training_frame=training_data)
      warn_phrase = "UserWarning"
      warn_string_of_interest = "slash (/) found"
      sys.stderr = (
          sys.__stderr__
      )  # redirect printout back to normal path
  
      try:  # for python 2.7
          assert len(buffer.buflist) == warnNumber
          if (
              len(buffer.buflist) > 0
          ):  # check to make sure we have the right number of warning
              for index in range(len(buffer.buflist)):
                  print(
                      "*** captured warning message: {0}".format(
                          buffer.buflist[index]
                      )
                  )
                  assert (warn_phrase in buffer.buflist[index]) and (
                      warn_string_of_interest in buffer.buflist[index]
                  )
      except:  # for python 3.
          if warnNumber == 0:
              try:
                  warns = buffer.getvalue()
                  assert False, "Warning not expected but received..."
              except:
                  assert True, "Warning not expected but received..."
          else:  # may receive more warnings than our own.  Need to filter it out
              warns = buffer.getvalue()
              print("*** captured warning message: {0}".format(warns))
              countWarns = warns.split().count("slash")
              assert (
                  countWarns == warnNumber
              ), "Expected number of warnings: {0}, but received {1}.".format(
                  warnNumber, countWarns
              )
  
imports: from builtins import range
  import sys, os
  import h2o
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  from tests import pyunit_utils
label_desc: Use image node_id 2 for calling a global function with example usage: check_warnings(model_id, X, Y, warnNumber, training_data) without return types