code: def testGLMCheckpointMultinomial():
      print("Checking checkpoint for multinomials....")
      train = h2o.import_file(
          pyunit_utils.locate(
              "smalldata/glm_test/multinomial_10_classes_10_cols_10000_Rows_train.csv"
          )
      )
      train["C1"] = train["C1"].asfactor()
      train["C2"] = train["C2"].asfactor()
      train["C3"] = train["C3"].asfactor()
      train["C4"] = train["C4"].asfactor()
      train["C5"] = train["C5"].asfactor()
      train["C11"] = train["C11"].asfactor()
      myY = "C11"
      mX = list(range(0, 10))
      buildModelCheckpointing(train, mX, myY, "multinomial", "irlsm")
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: testGLMCheckpointMultinomial() without return types