code: def test_infogram_personal_loan():
      """
      Test to make sure predictor can be specified using infogram model.
      """
      fr = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/admissibleml_test/Bank_Personal_Loan_Modelling.csv"
          )
      )
      target = "Personal Loan"
      fr[target] = fr[target].asfactor()
      x = [
          "Experience",
          "Income",
          "Family",
          "CCAvg",
          "Education",
          "Mortgage",
          "Securities Account",
          "CD Account",
          "Online",
          "CreditCard",
      ]
      infogram_model = H2OInfogram(
          seed=12345, protected_columns=["Age", "ZIP Code"]
      )
      infogram_model.train(x=x, y=target, training_frame=fr)
  
      glm_model1 = H2OGeneralizedLinearEstimator()
      glm_model1.train(
          x=infogram_model._extract_x_from_model(),
          y=target,
          training_frame=fr,
      )
      coef1 = glm_model1.coef()
      glm_model2 = H2OGeneralizedLinearEstimator()
      glm_model2.train(x=infogram_model, y=target, training_frame=fr)
      coef2 = glm_model2.coef()
  
      pyunit_utils.assertCoefDictEqual(coef1, coef2, tol=1e-6)
  
imports: import os
  import sys
  import h2o
  from h2o.estimators.infogram import H2OInfogram
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  from tests import pyunit_utils
label_desc: Use image node_id 1 for calling a global function with example usage: test_infogram_personal_loan() without return types