code: def testGLMCheckpointMultinomialLambdaSearch():
      print(
          "Checking checkpoint for multinomials with lambda_search...."
      )
      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))
      print(
          "Checking checkpoint for multinomials with lambda_search and without cold_start"
      )
      buildModelCheckpointing(
          train, mX, myY, "multinomial", "irlsm", False, 10
      )  # with cold_start
      print(
          "Checking checkpoint for multinomials with lambda_search and cold_start"
      )
      buildModelCheckpointing(
          train, mX, myY, "multinomial", "irlsm", True, 10
      )
  
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: testGLMCheckpointMultinomialLambdaSearch() without return types