code: def pubdev_random_cv():
      cars = h2o.import_file(
          path=pyunit_utils.locate("smalldata/junit/cars_20mpg.csv")
      )
      response_col = "economy"
      distribution = "gaussian"
      predictors = [
          "displacement",
          "power",
          "weight",
          "acceleration",
          "year",
      ]
  
      gbm1 = H2OGradientBoostingEstimator(
          nfolds=3, distribution=distribution, fold_assignment="Random"
      )
      gbm2 = H2OGradientBoostingEstimator(
          nfolds=3, distribution=distribution, fold_assignment="Random"
      )
      gbm1.train(y=response_col, x=predictors, training_frame=cars)
      gbm2.train(y=response_col, x=predictors, training_frame=cars)
  
      mse1 = gbm1.mse(xval=True)
      mse2 = gbm2.mse(xval=True)
      assert mse1 != mse2, (
          "The first model has an MSE of {0} and the second model has an MSE of {1}. Expected the "
          "first to be different from the second.".format(mse1, mse2)
      )
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: pubdev_random_cv() without return types