code: def test_infogram_iris_x_attributes():
      """
      Test to showcase that we can specify predictors using infogram model
      """
      fr = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/admissibleml_test/irisROriginal.csv"
          )
      )
      target = "Species"
      fr[target] = fr[target].asfactor()
      x = fr.names
      x.remove(target)
  
      infogram_model = H2OInfogram(
          seed=12345, distribution="multinomial"
      )  # build infogram model with default settings
      infogram_model.train(x=x, y=target, training_frame=fr)
  
      glm_model1 = H2OGeneralizedLinearEstimator(family="multinomial")
      glm_model1.train(
          x=infogram_model._extract_x_from_model(),
          y=target,
          training_frame=fr,
      )
      coef1 = glm_model1.coef()
      glm_model2 = H2OGeneralizedLinearEstimator(family="multinomial")
      glm_model2.train(x=infogram_model, y=target, training_frame=fr)
      coef2 = glm_model2.coef()
      coef_classes = coef1.keys()
      for key in coef_classes:
          pyunit_utils.assertCoefDictEqual(
              coef1[key], coef2[key], 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_iris_x_attributes() without return types