code: def test_ordinal_gridsearch():
      yindex = "C11"
      Dtrain = h2o.import_file(
          pyunit_utils.locate(
              "bigdata/laptop/glm_ordinal_logit/ordinal_multinomial_training_set.csv"
          )
      )
      Dtrain[yindex] = Dtrain[yindex].asfactor()
  
      hyper_parameters = {
          "alpha": [0.01, 0.3, 0.5],
          "lambda": [1e-5, 1e-6, 1e-7, 1e-8],
      }
      gs = H2OGridSearch(
          H2OGeneralizedLinearEstimator(family="ordinal"),
          grid_id="ordinal_grid",
          hyper_params=hyper_parameters,
      )
      gs.train(x=list(range(0, 10)), y="C11", training_frame=Dtrain)
      gs.show()
  
imports: import sys
  from tests import pyunit_utils
  import h2o
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  from h2o.grid.grid_search import H2OGridSearch
label_desc: Use image node_id 1 for calling a global function with example usage: test_ordinal_gridsearch() without return types