code: def std_coef_plot_test():
      kwargs = {}
      kwargs["server"] = True
  
      # 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,
      )
  
      # Plot GLM standardized coefficient magnitudes and check that num_of_features accepts input
      cars_glm.std_coef_plot(server=True)
      cars_glm.std_coef_plot(num_of_features=2, server=True)
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: std_coef_plot_test() without return types