code: def testGLMCheckpointBinomial():
      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))
  
      solvers = ["irlsm"]
      for solver in solvers:
          print(
              "Checking checkpoint for binomials with solver {0}".format(
                  solver
              )
          )
          buildModelCheckpointing(train, X, Y, "binomial", solver)
  
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: testGLMCheckpointBinomial() without return types