code: def testGLMCheckpointGaussianLambdaSearch():
      print(
          "Checking checkpoint for regression with lambda search ...."
      )
      h2o_data = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/glm_test/gaussian_20cols_10000Rows.csv"
          )
      )
      enum_columns = [
          "C1",
          "C2",
          "C3",
          "C4",
          "C5",
          "C6",
          "C7",
          "C8",
          "C9",
          "C10",
      ]
      for cname in enum_columns:
          h2o_data[cname] = h2o_data[cname]
      myY = "C21"
      myX = list(range(20))
      print("Setting cold_start to false")
      buildModelCheckpointing(
          h2o_data, myX, myY, "gaussian", "irlsm", False
      )
      print("Setting cold_start to true")
      buildModelCheckpointing(
          h2o_data, myX, myY, "gaussian", "irlsm", True
      )
  
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: testGLMCheckpointGaussianLambdaSearch() without return types