code: def glm_alpha_lambda_arrays_last_lambda_best():
      df = h2o.import_file(
          path=pyunit_utils.locate("smalldata/logreg/prostate.csv")
      )
      df["CAPSULE"] = df["CAPSULE"].asfactor()
      glm = H2OGeneralizedLinearEstimator(
          family="binomial",
          lambda_=[0.9, 0.2, 0.8, 0.5, 0.1, 0.099, 0.098],
          alpha=[0.1, 0.5, 0.2, 0.9, 0.8],
          nfolds=5,
          seed=1234,
      )
      glm.train(training_frame=df, y="CAPSULE")
      assert glm._model_json["output"]["alpha_best"] == 0.1
      assert glm._model_json["output"]["lambda_best"] == 0.098
      assert glm._model_json["output"]["lambda_1se"] == 0.1
  
imports: 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: glm_alpha_lambda_arrays_last_lambda_best() without return types