code: def testGLMCheckpointBinomialLambdaSearch():
      train = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/glm_test/binomial_20_cols_10KRows.csv"
          )
      )
      for ind in range(10):
          train[ind] = train[ind].asfactor()
      train["C21"] = train["C21"].asfactor()
      Y = "C21"
      X = list(range(0, 20))
      solver = "irlsm"
      print(
          "Checking checkpoint for binomials with solver {0} and lambda_search".format(
              solver
          )
      )
      print("setting cold_start to false")
      buildModelCheckpointing(
          train, X, Y, "binomial", solver, False, 10
      )  # without cold start
      print("setting cold_start to true")
      buildModelCheckpointing(train, X, Y, "binomial", solver, 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: testGLMCheckpointBinomialLambdaSearch() without return types