code: def pyunit_make_glm_model():
      # TODO: PUBDEV-1717
      pros = h2o.import_file(
          pyunit_utils.locate("smalldata/prostate/prostate.csv")
      )
  
      model = H2OGeneralizedLinearEstimator(
          family="gaussian", alpha=[0]
      )
      model.train(
          x=["AGE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"],
          y="CAPSULE",
          training_frame=pros,
      )
      # model = h2o.glm(x=pros[["AGE","DPROS","DCAPS","PSA","VOL","GLEASON"]], y=pros["CAPSULE"], family="gaussian", alpha=[0])
      new_betas = {
          "AGE": 0.5,
          "DPROS": 0.5,
          "DCAPS": 0.5,
          "PSA": 0.5,
          "VOL": 0.5,
          "GLEASON": 0.5,
      }
  
      names = "["
      for n in list(new_betas.keys()):
          names += '"' + n + '",'
      names = names[0 : len(names) - 1] + "]"
      betas = "["
  
      for b in list(new_betas.values()):
          betas += str(b) + ","
      betas = betas[0 : len(betas) - 1] + "]"
      res = h2o.H2OConnection.post_json(
          "MakeGLMModel", model=model._id, names=names, beta=betas
      )
  
imports: from builtins import str
  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: pyunit_make_glm_model() without return types