code: def test_glrm_transform():
      # generate training and test frames
      m = 1000
      n = 100
      k = 8
      np.random.seed(12345)
  
      print(
          "Uploading random uniform matrix with rows = "
          + str(m)
          + " and cols = "
          + str(n)
      )
      Y = np.random.rand(k, n)
      X = np.random.rand(m, k)
      train = np.dot(X, Y)
      train_h2o = h2o.H2OFrame(train.tolist())
      frames = train_h2o.split_frame(ratios=[0.9])
      train = frames[0]
      test = frames[1]
  
      glrm_h2o = H2OGeneralizedLowRankEstimator(
          k=k, loss="Quadratic", seed=12345
      )
      glrm_h2o.train(x=train_h2o.names, training_frame=train)
      predFrame = glrm_h2o.predict(test)
      xFrame = glrm_h2o.transform_frame(test)
  
      glrm_h2o2 = H2OGeneralizedLowRankEstimator(
          k=k, loss="Quadratic", seed=12345
      )
      glrm_h2o2.train(x=train_h2o.names, training_frame=train)
      xFrame2 = glrm_h2o2.transform_frame(test)
  
      assert predFrame.nrows == xFrame.nrows, (
          "predictor frame number of row: {0}, transform frame number of row: "
          "{1}".format(predFrame.nrows, xFrame.nrows)
      )
      pyunit_utils.compare_frames_local(
          xFrame, xFrame2, prob=1.0, tol=1e-6
      )
  
imports: from builtins import str
  import sys
  import h2o
  from tests import pyunit_utils
  import numpy
  from h2o.estimators.glrm import H2OGeneralizedLowRankEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_glrm_transform() without return types