code: def glrm_export():
      print("###### GLRM ######")
      frame = h2o.upload_file(
          pyunit_utils.locate("smalldata/prostate/prostate_cat.csv")
      )
      model = H2OGeneralizedLowRankEstimator(
          k=8, init="svd", recover_svd=True
      )
      model.train(x=frame.names, training_frame=frame)
      expect_error(model.download_pojo, model="GLRM", format="POJO")
      model.download_mojo(path=RESULT_DIR)
  
imports: import sys
  import random
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.deeplearning import H2ODeepLearningEstimator
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  from h2o.estimators.glrm import H2OGeneralizedLowRankEstimator
  from h2o.estimators.kmeans import H2OKMeansEstimator
  from h2o.estimators.naive_bayes import H2ONaiveBayesEstimator
  from h2o.estimators.pca import H2OPrincipalComponentAnalysisEstimator
  from h2o.estimators.random_forest import H2ORandomForestEstimator
  from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
  from h2o.estimators.word2vec import H2OWord2vecEstimator
  from h2o.exceptions import H2OValueError
label_desc: Use image node_id 5 for calling a global function with example usage: glrm_export() without return types