code: def get_hyperparams_dict_return_correct_params():
      prostate_train = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/logreg/prostate_train.csv"
          )
      )
      prostate_train["CAPSULE"] = prostate_train["CAPSULE"].asfactor()
  
      num_folds = random.randint(2, 5)
      fold_assignments = h2o.H2OFrame(
          [
              [random.randint(0, num_folds - 1)]
              for _ in range(prostate_train.nrow)
          ]
      )
      fold_assignments.set_names(["fold_assignments"])
      prostate_train = prostate_train.cbind(fold_assignments)
  
      x_features = range(1, prostate_train.ncol)
      y_target = "CAPSULE"
      h2o_data_frame = prostate_train
  
      grid = H2OGridSearch(
          model=H2OGradientBoostingEstimator,
          hyper_params={
              "fold_assignment": ["Stratified"],
              "sample_rate_per_class": [[1.0, 0.6]],
          },
          search_criteria={
              "strategy": "RandomDiscrete",
              "max_models": 1,
          },
      )
      grid.train(
          x=x_features,
          y=y_target,
          training_frame=h2o_data_frame,
          nfolds=num_folds,
      )
  
      print(grid.get_grid())
      hyperparams_dict = grid.get_hyperparams_dict(0)
      assert hyperparams_dict["fold_assignment"] == "Stratified"
      assert hyperparams_dict["sample_rate_per_class"] == [1.0, 0.6]
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators import H2OGradientBoostingEstimator
  from h2o.grid.grid_search import H2OGridSearch
  import random
label_desc: Use image node_id 1 for calling a global function with example usage: get_hyperparams_dict_return_correct_params() without return types