code: def glrm_mojo():
      h2o.remove_all()
      # dataset with numerical values only
      print(
          "Checking GLRM predict performance with numerical dataset....."
      )
      train = h2o.import_file(
          pyunit_utils.locate("smalldata/logreg/benign.csv")
      )
      test = h2o.import_file(
          pyunit_utils.locate("smalldata/logreg/benign.csv")
      )
      get_glrm_xmatrix(train, test, K=3, tol=1e-1)
  
      # dataset with enum and numerical columns
      print("Checking GLRM predict performance with mixed dataset.....")
      train = h2o.import_file(
          pyunit_utils.locate("smalldata/prostate/prostate_cat.csv")
      )
      test = h2o.import_file(
          pyunit_utils.locate("smalldata/prostate/prostate_cat.csv")
      )
      get_glrm_xmatrix(train, test, K=5, compare_predict=False, tol=1.5)
  
file_path: h2o-3/h2o-py/tests/testdir_javapredict/pyunit_pubdev_5826_glrm_predict_large.py
imports: import sys, os
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glrm import H2OGeneralizedLowRankEstimator
  from random import randint
  import re
label_desc: Use image node_id 1 for calling a global function with example usage: glrm_mojo() without return types