code: def glrm_catagorical_bug_fix():
      print("Importing prostate.csv data...")
  
      tbl2 = H2OTwoDimTable(
          cell_values=[[1, 2, 4]] * 10,
          col_header=["q1", "q2", "q3"],
          row_header=range(10),
          table_header="Table 2",
      )
  
      # H2OTwoDimTable containing the correct archetype values run before Wendy optimized memory for GLRM
      cell_values = [
          [
              "Arch1",
              0.0,
              1.0,
              0.0,
              0.0,
              0.0,
              0.0,
              1.0,
              1.0,
              0.0,
              1.0,
              0.0,
              58.295918367346935,
              8.810102040816325,
              11.344897959183678,
              6.285714285714286,
          ],
          [
              "Arch2",
              0.0,
              0.0,
              1.0,
              0.0,
              0.0,
              0.0,
              1.0,
              1.0,
              0.0,
              1.0,
              0.0,
              69.35514018691589,
              7.538224299065424,
              10.087757009345797,
              5.6168224299065415,
          ],
          [
              "Arch3",
              1.0,
              0.0,
              0.0,
              0.0,
              0.0,
              0.0,
              1.0,
              0.0,
              1.0,
              1.0,
              0.0,
              64.68,
              75.892,
              10.812000000000001,
              7.44,
          ],
          [
              "Arch4",
              0.0,
              1.0,
              0.0,
              0.0,
              0.0,
              0.0,
              1.0,
              1.0,
              0.0,
              1.0,
              0.0,
              68.77083333333333,
              13.368750000000002,
              49.44583333333334,
              5.9375,
          ],
          [
              "Arch5",
              0.0,
              0.0,
              0.0,
              1.0,
              0.0,
              0.0,
              1.0,
              0.0,
              1.0,
              1.0,
              0.0,
              69.04901960784314,
              16.140196078431373,
              11.510000000000005,
              7.235294117647059,
          ],
      ]
      col_header = [
          "dprosboth",
          "dprosleft",
          "dprosnone",
          "dprosright",
          "raceblack",
          "racena",
          "racewhite",
          "capsuleno",
          "capsuleyes",
          "dcapsno",
          "dcapsyes",
          "age",
          "psa",
          "vol",
          "gleason",
      ]
      row_header = ["Arch1", "Arch2", "Arch3", "Arch4", "Arch5"]
      table_header = "archetypes"
      correct_archetype = H2OTwoDimTable(
          cell_values=cell_values,
          col_header=col_header,
          row_header=row_header,
          table_header=table_header,
      )
  
      prostateF = h2o.upload_file(
          pyunit_utils.locate("smalldata/prostate/prostate_cat.csv")
      )
  
      glrm_h2o = H2OGeneralizedLowRankEstimator(
          k=5, recover_svd=True, seed=1234
      )
      glrm_h2o.train(x=prostateF.names, training_frame=prostateF)
      glrm_h2o.show()
  
      assert pyunit_utils.equal_2d_tables(
          glrm_h2o._model_json["output"]["archetypes"]._cell_values,
          correct_archetype._cell_values,
          tolerance=1e-4,
      ), "GLRM model archetypes generated from current model are not correct."
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glrm import H2OGeneralizedLowRankEstimator
  from h2o.two_dim_table import H2OTwoDimTable
label_desc: Use image node_id 1 for calling a global function with example usage: glrm_catagorical_bug_fix() without return types