code: def glrm_arrests():
      print("Importing USArrests.csv data...")
      arrestsH2O = h2o.upload_file(
          pyunit_utils.locate("smalldata/pca_test/USArrests.csv")
      )
  
      pca_h2o = H2OPCA(k=4, transform="STANDARDIZE")
      pca_h2o.train(x=list(range(4)), training_frame=arrestsH2O)
      pca_h2o.summary()
      pca_h2o.show()
  
      print("H2O GLRM on standardized data with quadratic loss:\n")
      glrm_h2o = H2OGeneralizedLowRankEstimator(
          k=4,
          transform="STANDARDIZE",
          loss="Quadratic",
          gamma_x=0,
          gamma_y=0,
          init="SVD",
          recover_svd=True,
      )
      glrm_h2o.train(x=arrestsH2O.names, training_frame=arrestsH2O)
      glrm_h2o.show()
  
      # compare table values and make sure they are the same between PCA and GLRM
      assert pyunit_utils.equal_2d_tables(
          pca_h2o._model_json["output"]["importance"]._cell_values,
          glrm_h2o._model_json["output"]["importance"]._cell_values,
          tolerance=1e-4,
      ), "PCA and GLRM variance metrics do not agree.  Fix it please."
  
      sys.stdout.flush()
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glrm import H2OGeneralizedLowRankEstimator
  from h2o.estimators.pca import H2OPrincipalComponentAnalysisEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: glrm_arrests() without return types