code: def test_GLM_technical_note_warning():
      hdf = h2o.upload_file(
          pyunit_utils.locate(
              "smalldata/prostate/prostate_complete.csv.zip"
          )
      )
      test = h2o.upload_file(
          pyunit_utils.locate(
              "smalldata/prostate/prostate_complete.csv.zip"
          )
      )
      wt = pyunit_utils.random_dataset_real_only(
          hdf.nrow, 1, misFrac=0, randSeed=12345
      )
      wt = wt.abs()
      wt.set_name(0, "weights")
      hdf = hdf.cbind(wt)
  
      y = "AGE"
      x = ["RACE", "DCAPS", "PSA", "VOL", "DPROS", "GLEASON"]
  
      model_h2o_tweedie = H2OGeneralizedLinearEstimator(
          weights_column="weights"
      )
      model_h2o_tweedie.train(
          x=x, y=y, training_frame=hdf, validation_frame=test
      )  # this should generate a warning message
  
      # check and make sure we get the correct warning message
      warn_phrase = "https://github.com/h2oai/h2o-3/discussions/15512"
      pyunit_utils.checkLogWarning(warn_phrase, wantWarnMessage=True)
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_GLM_technical_note_warning() without return types