code: def testvarimp():
      print("Checking variable importance for binomials....")
      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]
      buildModelCheckVarimp(training_data, X, Y, "binomial")
  
      print("Checking variable importance for multinomials....")
      train = h2o.import_file(
          path=pyunit_utils.locate("smalldata/iris/iris_wheader.csv")
      )
      myY = "class"
      mX = list(range(0, 4))
      buildModelCheckVarimp(train, mX, myY, "multinomial")
  
      print("Checking variable importance for regression....")
      h2o_data = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/prostate/prostate_complete.csv.zip"
          )
      )
      myY = "GLEASON"
      myX = [
          "ID",
          "AGE",
          "RACE",
          "CAPSULE",
          "DCAPS",
          "PSA",
          "VOL",
          "DPROS",
      ]
      buildModelCheckVarimp(h2o_data, myX, myY, "gaussian")
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: testvarimp() without return types