code: def test_benign():
      training_data = h2o.import_file(
          pyunit_utils.locate("smalldata/logreg/benign.csv")
      )
  
      Y = 3
      X = [0, 1, 2, 4, 5, 6, 7, 8, 9, 10]
  
      model = H2OGeneralizedLinearEstimator(
          family="binomial", alpha=0, Lambda=1e-5
      )
      model = H2OGeneralizedLinearEstimator(
          family="binomial", alpha=0, lambda_=1e-5
      )
      model.train(x=X, y=Y, training_frame=training_data)
  
      in_names = [training_data.names[i] for i in X]
      out_names = [
          model._model_json["output"]["coefficients_table"].cell_values[
              c
          ][0]
          for c in range(len(X) + 1)
      ]
      assert in_names == out_names[1:]
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_benign() without return types