code: def getLambdaModel():
      print("Read data")
      prostate = h2o.import_file(
          path=pyunit_utils.locate("smalldata/logreg/prostate.csv")
      )
  
      myX = ["AGE", "RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"]
      myY = "CAPSULE"
      family = random.choice(["gaussian", "binomial"])
      print(family)
  
      print("Do lambda search and build models")
      if family == "gaussian":
          # model = h2o.glm(x=prostate[myX], y=prostate[myY], family=family, standardize=True, use_all_factor_levels=True, lambda_search=True)
          model = H2OGeneralizedLinearEstimator(
              family=family,
              standardize=True,
              use_all_factor_levels=True,
              lambda_search=True,
          )
          model.train(x=myX, y=myY, training_frame=prostate)
      else:
          prostate[myY] = prostate[myY].asfactor()
          model = H2OGeneralizedLinearEstimator(
              family=family,
              standardize=True,
              use_all_factor_levels=True,
              lambda_search=True,
          )
          model.train(x=myX, y=myY, training_frame=prostate)
          # model = h2o.glm(x=prostate[myX], y=prostate[myY].asfactor(), family=family, standardize=True, use_all_factor_levels=True, lambda_search=True)
  
      print("the models were built over the following lambda values: ")
      all_lambdas = model.models(1).lambda_all()
      print(all_lambdas)
  
      for i in range(10):
          Lambda = random.sample(all_lambdas, 1)
          print("For Lambda we get this model:")
          m1 = h2o.getGLMLambdaModel(
              model.models(
                  random.randint(0, len(model.models() - 1)),
                  Lambda=Lambda,
              )
          )
          m1.show()
          print("this model should be same as the one above:")
          m2 = h2o.getGLMLambdaModel(
              model.models(
                  random.randint(0, len(model.models() - 1)),
                  Lambda=Lambda,
              )
          )
          m2.show()
          assert m1 == m2, "expected models to be equal"
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  import random
label_desc: Use image node_id 1 for calling a global function with example usage: getLambdaModel() without return types