code: def test_negBinomial_makeGLMModel():
      print("Read in prostate data.")
      h2o_data = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/prostate/prostate_complete.csv.zip"
          )
      )
      print("Testing for family: Negative Binomial")
      print("Set variables for h2o.")
      myY = "GLEASON"
      myX = [
          "ID",
          "AGE",
          "RACE",
          "CAPSULE",
          "DCAPS",
          "PSA",
          "VOL",
          "DPROS",
      ]
  
      thetas = [0.000000001, 0.01, 0.1, 0.5, 1]
      for thetaO in thetas:
          h2o_model_log = H2OGeneralizedLinearEstimator(
              family="negativebinomial",
              link="log",
              alpha=0.5,
              Lambda=0.0001,
              theta=thetaO,
          )
          h2o_model_log.train(x=myX, y=myY, training_frame=h2o_data)
          predictModel = h2o_model_log.predict(h2o_data)
          r = H2OGeneralizedLinearEstimator.getGLMRegularizationPath(
              h2o_model_log
          )
          makeModel = H2OGeneralizedLinearEstimator.makeGLMModel(
              model=h2o_model_log, coefs=r["coefficients"][0]
          )  # model generated from setting coefficients to model
          predictMake = makeModel.predict(h2o_data)
          pyunit_utils.compare_frames_local(
              predictModel, predictMake, prob=1
          )
  
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_negBinomial_makeGLMModel() without return types