code: def test_big_data_cars():
      """
      Test big data dataset, with metric logloss.
      """
      h2o_df = h2o.import_file(
          path=pyunit_utils.locate(
              "bigdata/laptop/lending-club/loan.csv"
          )
      )
      predictors = h2o_df.col_names
      response_col = h2o_df.col_names[12]  # loan amount
      predictors.remove(response_col)
  
      model = H2OGeneralizedLinearEstimator(family="binomial")
      model.train(y=response_col, x=predictors, training_frame=h2o_df)
  
      metric = "logloss"
  
      pm_h2o_df = model.permutation_importance(
          h2o_df, use_pandas=True, n_samples=-1, metric=metric
      )
      for pred in predictors:
          if pred == "Variable":
              continue
          assert isinstance(
              pm_h2o_df.loc[pred, "Relative Importance"], float
          )  # Relative PFI
  
      pm_h2o_df = model.permutation_importance(
          h2o_df, use_pandas=True, n_samples=100, metric=metric
      )
      for pred in predictors:
          if pred == "Variable":
              continue
          assert isinstance(
              pm_h2o_df.loc[pred, "Relative Importance"], float
          )
  
file_path: h2o-3/h2o-py/tests/testdir_jira/pyunit_pubdev_7139_permutation_var_imp.py
imports: import sys, os
  import h2o
  from h2o.utils.typechecks import is_type
  from h2o.estimators import H2OGradientBoostingEstimator, H2OGeneralizedLinearEstimator
  from tests import pyunit_utils
label_desc: Use image node_id 3 for calling a global function with example usage: test_big_data_cars() without return types