code: def test_glm_params():
      H2OGeneralizedLinearEstimator()
      H2OGeneralizedLinearEstimator(nfolds=5, seed=1000, alpha=0.5)
  
      df = h2o.H2OFrame.from_python(
          {
              "response": [1, 2, 3, 4, 5],
              "a": [0, 1, 0, 1, 0],
              "b": [-1, 3, 7, 11, 20],
              "n": [0] * 5,
              "w": [1] * 5,
          }
      )
  
      model = H2OGeneralizedLinearEstimator()
      model.training_frame = df
      model.validation_frame = df
      model.nfolds = 3
      model.keep_cross_validation_predictions = True
      model.keep_cross_validation_fold_assignment = True
      model.fold_assignment = "random"
      model.fold_column = "b"
      model.response_column = "response"
      model.ignored_columns = ["x", "y"]
      model.ignore_const_cols = True
      model.score_each_iteration = True
      model.offset_column = "n"
      model.weights_column = "w"
      model.family = "MultiNomial"
      model.family = "GAUSSIAN"
      model.family = "Twee-die"
      model.family = "'poIssoN'"
      model.tweedie_variance_power = 1
      model.tweedie_link_power = 2
      model.solver = "CoordinateDescentNaive"
  
      try:
          model.fold_assignment = "pseudo-random"
          assert False
      except H2OTypeError:
          pass
  
      try:
          model.ignored_columns = "c"
          assert False
      except H2OTypeError:
          pass
  
imports: import h2o
  from h2o.estimators import H2OGeneralizedLinearEstimator
  from h2o.exceptions import H2OTypeError
  from tests import pyunit_utils
label_desc: Use image node_id 1 for calling a global function with example usage: test_glm_params() without return types