code: def test_glrm_seeds():
      print("Importing iris_wheader.csv data...")
      irisH2O = h2o.upload_file(
          pyunit_utils.locate("smalldata/iris/iris_wheader.csv")
      )
      irisH2O.describe()
      initMethods = [
          "random",
          "svd",
          "plus_plus",
          "user",
      ]  # user mode without init values is equivalent to randomized
      seeds = [123456789, 987654321]
  
      for initM in initMethods:
          # first two models are trained with same seed and should be the same
          glrm_h2o_seed0 = setupTrainModel(initM, seeds[0])
          predict_seed0 = predGLRM(irisH2O, glrm_h2o_seed0)
  
          glrm_h2o_seed0Same = setupTrainModel(initM, seeds[0])
          predict_seed0Same = predGLRM(irisH2O, glrm_h2o_seed0Same)
  
          # trained with same seed, reconstructed datasets should be the same
          pyunit_utils.compare_frames_local(
              predict_seed0[0:4], predict_seed0Same[0:4], prob=1.0
          )  # compare and make sure reconstructed frames are the same
  
          # trained with different seed, reconstructed datasets should be different
          glrm_h2o_seed1 = setupTrainModel(initM, seeds[1])
          predict_seed1 = predGLRM(irisH2O, glrm_h2o_seed1)
          assert not (
              pyunit_utils.compare_frames_local(
                  predict_seed0[0:4],
                  predict_seed1[0:4],
                  prob=1.0,
                  returnResult=True,
              )
          ), "GLRM return same results with different random seed."
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glrm import H2OGeneralizedLowRankEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_glrm_seeds() without return types