code: def test_makeGLMModel():
      # read in the dataset and construct training set (and validation set)
      d = h2o.import_file(
          path=pyunit_utils.locate("smalldata/logreg/prostate.csv")
      )
      m = glm(
          family="binomial",
          Lambda=[0.001],
          alpha=[0.5],
          solver="COORDINATE_DESCENT",
      )
      m.train(training_frame=d, x=[2, 3, 4, 5, 6, 7, 8], y=1)
      r = glm.getGLMRegularizationPath(m)
      m2 = glm.makeGLMModel(model=m, coefs=r["coefficients"][0])
      f1 = m.predict(d)  # predict with original model
      f2 = m2.predict(d)  # predict with model out of makeGLMModel
      pyunit_utils.compare_frames_local(f1[1], f2[1], prob=1)
      coefs = r["coefficients"][0]
      coefs["wendy_dreams"] = 8
  
      try:
          glm.makeGLMModel(model=m, coefs=coefs)
          assert (
              False
          ), "Test failed: should have throw exception of bad coefficient length!"
      except Exception as ex:
          print(ex)
          temp = str(ex)
          assert (
              "Server error java.lang.IllegalArgumentException:" in temp
          ) and (
              "model coefficient length 8 is different from coefficient provided by user "
          ) in temp, "Wrong exception was received."
          print("makeGLMModel test passed!")
  
imports: import sys
  import h2o
  from builtins import range
  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_makeGLMModel() without return types