code: def test_binomial_alpha():
      training_data = h2o.import_file(
          pyunit_utils.locate("smalldata/logreg/benign.csv")
      )
      Y = 3
      X = [0, 1, 2, 4, 5, 6, 7, 8, 9, 10]
  
      # test with lambda search on, generate_scoring_history on and off
      model1 = H2OGeneralizedLinearEstimator(
          family="binomial",
          alpha=[0, 0.2, 1],
          lambda_search=True,
          generate_scoring_history=True,
          nlambdas=5,
      )
      model1.train(x=X, y=Y, training_frame=training_data)
      model2 = H2OGeneralizedLinearEstimator(
          family="binomial",
          alpha=[0, 0.2, 1],
          lambda_search=True,
          generate_scoring_history=True,
          nlambdas=5,
      )
      model2.train(x=X, y=Y, training_frame=training_data)
      pyunit_utils.assertCoefDictEqual(model1.coef(), model2.coef())
  
      # test with lambda search off, generate_scoring_history on and off
      model1 = H2OGeneralizedLinearEstimator(
          family="binomial",
          alpha=[0, 0.8, 1],
          lambda_search=False,
          generate_scoring_history=True,
          Lambda=[0, 0.1],
      )
      model1.train(x=X, y=Y, training_frame=training_data)
      model2 = H2OGeneralizedLinearEstimator(
          family="binomial",
          alpha=[0, 0.8, 1],
          lambda_search=False,
          generate_scoring_history=True,
          Lambda=[0, 0.1],
      )
      model2.train(x=X, y=Y, training_frame=training_data)
      pyunit_utils.assertCoefDictEqual(model1.coef(), model2.coef())
  
      # test with lambda search on, generate_scoring_history on and off, cv on
      model1 = H2OGeneralizedLinearEstimator(
          family="binomial",
          alpha=[0, 0.8, 1],
          lambda_search=True,
          generate_scoring_history=True,
          nfolds=2,
          seed=12345,
          nlambdas=5,
      )
      model1.train(x=X, y=Y, training_frame=training_data)
      model2 = H2OGeneralizedLinearEstimator(
          family="binomial",
          alpha=[0, 0.8, 1],
          lambda_search=True,
          generate_scoring_history=True,
          nfolds=2,
          seed=12345,
          nlambdas=5,
      )
      model2.train(x=X, y=Y, training_frame=training_data)
      pyunit_utils.assertCoefDictEqual(model1.coef(), model2.coef())
  
      # test with lambda search off, generate_scoring_history on and off, cv on
      model1 = H2OGeneralizedLinearEstimator(
          family="binomial",
          alpha=[0, 0.2, 1],
          lambda_search=False,
          generate_scoring_history=True,
          nfolds=2,
          seed=12345,
          Lambda=[0, 0.001],
      )
      model1.train(x=X, y=Y, training_frame=training_data)
      model2 = H2OGeneralizedLinearEstimator(
          family="binomial",
          alpha=[0, 0.2, 1],
          lambda_search=False,
          generate_scoring_history=True,
          nfolds=2,
          seed=12345,
          Lambda=[0, 0.001],
      )
      model2.train(x=X, y=Y, training_frame=training_data)
      pyunit_utils.assertCoefDictEqual(model1.coef(), model2.coef())
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_binomial_alpha() without return types