code: def perfect_separation_balanced():
      print("Read in synthetic balanced dataset")
      data = h2o.import_file(
          pyunit_utils.locate(
              "smalldata/synthetic_perfect_separation/balanced.csv"
          ),
          col_names=["y", "x1", "x2"],
      )
      print(data)
  
      print("Fit model on dataset")
      model = H2OGeneralizedLinearEstimator(
          family="binomial", lambda_search=True, alpha=0.5, lambda_=1e-8
      )
      model.train(x=["x1", "x2"], y="y", training_frame=data)
  
      print(
          "Extract model coefficients and assert reasonable values (ie. no greater than 50)"
      )
      print("Balanced dataset")
      coef = [
          c[1]
          for c in model._model_json["output"][
              "coefficients_table"
          ].cell_values
          if c[0] != "Intercept"
      ]
      for c in coef:
          assert c < 50, "coefficient is too large"
  
label_desc: Use image node_id 1 for calling a global function with example usage: perfect_separation_balanced() without return types