code: def pubdev_6431_deleted_key():
      jobs_count_start = len(h2o.cluster().list_jobs()[0])
  
      input_frame = h2o.import_file(
          pyunit_utils.locate("smalldata/flow_examples/abalone.csv.gz")
      )
      model = H2OGeneralizedLinearEstimator(alpha=0.3)
      model.train(
          y="C9",
          training_frame=input_frame,
          validation_frame=input_frame,
      )
      assert (jobs_count_start + 2) == len(
          h2o.cluster().list_jobs()[0]
      ), "(after train) expected {0} jobs but found {1} - {2}".format(
          (jobs_count_start + 2),
          len(h2o.cluster().list_jobs()[0]),
          h2o.cluster().list_jobs(),
      )
  
      h2o.remove(model)
      assert (jobs_count_start + 2) == len(
          h2o.cluster().list_jobs()[0]
      ), "(after rm) expected {0} jobs but found {1} - {2}".format(
          (jobs_count_start + 2),
          len(h2o.cluster().list_jobs()[0]),
          h2o.cluster().list_jobs(),
      )
  
      hyper_parameters = {
          "alpha": [0, 0.3],
      }
      grid_search = H2OGridSearch(
          H2OGeneralizedLinearEstimator, hyper_params=hyper_parameters
      )
      grid_search.train(
          y="C9",
          training_frame=input_frame,
          validation_frame=input_frame,
      )
      assert (jobs_count_start + 3) == len(
          h2o.cluster().list_jobs()[0]
      ), "(after grid) expected {0} jobs but found {1}- {2}".format(
          (jobs_count_start + 3),
          len(h2o.cluster().list_jobs()[0]),
          h2o.cluster().list_jobs(),
      )
  
imports: import h2o
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  from h2o.grid.grid_search import H2OGridSearch
  from tests import pyunit_utils
label_desc: Use image node_id 1 for calling a global function with example usage: pubdev_6431_deleted_key() without return types