code: def std_coef__varimp():
      # import data set
      cars = h2o.import_file(
          pyunit_utils.locate("smalldata/junit/cars_20mpg.csv")
      )
  
      # Constructing validation and train sets by sampling (20/80)
      s = cars[0].runif()
      cars_train = cars[s <= 0.8]
      cars_valid = cars[s > 0.8]
  
      # set list of features, target, and convert target to factor
      predictors = [
          "displacement",
          "power",
          "weight",
          "acceleration",
          "year",
      ]
      response_col = "economy_20mpg"
      cars[response_col] = cars[response_col].asfactor()
  
      # Build and train a GLM model
      cars_glm = H2OGeneralizedLinearEstimator()
      cars_glm.train(
          x=predictors,
          y=response_col,
          training_frame=cars_train,
          validation_frame=cars_valid,
      )
  
      # test saving:
      with TemporaryDirectory() as tmpdir:
          path = "{}/plot1.png".format(tmpdir)
          test_plot_result_saving(
              cars_glm.std_coef_plot(server=True),
              "{}/plot2.png".format(tmpdir),
              cars_glm.std_coef_plot(server=True, save_plot_path=path),
              path,
          )
  
          test_plot_result_saving(
              cars_glm.varimp_plot(server=True),
              "{}/plot2.png".format(tmpdir),
              cars_glm.varimp_plot(server=True, save_plot_path=path),
              path,
          )
  
imports: import sys
  import matplotlib
  from h2o.automl import H2OAutoML
  from h2o.estimators import H2OGradientBoostingEstimator
  from h2o.exceptions import H2OError
  from h2o.transforms import H2OPCA
  import h2o
  from tests import pyunit_utils, test_plot_result_saving, TemporaryDirectory
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator, H2OGeneralizedLinearEstimator
  from h2o.plot import decorate_plot_result, RAISE_ON_FIGURE_ACCESS
label_desc: Use image node_id 9 for calling a global function with example usage: std_coef__varimp() without return types