code: def testFrameTransform():
      train = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/prostate/prostate_complete.csv.zip"
          )
      )
      y = "CAPSULE"
      x = ["AGE", "VOL", "DCAPS"]
      train[10, 2] = None
      train[20, 7] = None
      train[y] = train[y].asfactor()
      # build model choosing skip
      model1 = H2OANOVAGLMEstimator(
          family="binomial", lambda_=0, missing_values_handling="skip"
      )
      model1.train(x=x, y=y, training_frame=train)
      # build model deleting the two rows with missing values
      train.drop([10, 20], axis=0)
      model2 = H2OANOVAGLMEstimator(
          family="binomial", lambda_=0, missing_values_handling="skip"
      )
      model2.train(x=x, y=y, training_frame=train)
      # the two models should be the same, compare the model summaries
      summary1 = model1._model_json["output"]["model_summary"]
      summary2 = model2._model_json["output"]["model_summary"]
      pyunit_utils.assert_H2OTwoDimTable_equal_upto(
          summary1, summary2, summary1.col_header
      )
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.anovaglm import H2OANOVAGLMEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: testFrameTransform() without return types