code: def offset_1388():
      print("Loading datasets...")
      pros_hex = h2o.import_file(
          pyunit_utils.locate("smalldata/prostate/prostate.csv")
      )
      pros_hex[1] = pros_hex[1].asfactor()
      pros_hex[3] = pros_hex[3].asfactor()
      pros_hex[4] = pros_hex[4].asfactor()
      pros_hex[5] = pros_hex[5].asfactor()
      pros_hex[8] = pros_hex[8].asfactor()
  
      cars_hex = h2o.import_file(
          pyunit_utils.locate("smalldata/junit/cars.csv")
      )
      cars_hex[0] = cars_hex[0].asfactor()
      cars_hex[2] = cars_hex[2].asfactor()
  
      print("Running Binomial Comparison...")
      glm_bin_h2o = h2o.glm(
          x=pros_hex[2:9],
          y=pros_hex[1],
          training_frame=pros_hex,
          family="binomial",
          standardize=False,
          offset_column="AGE",
          Lambda=[0],
          max_iterations=100,
      )
      print("binomial")
      print("R:")
      print("deviance: {0}".format(1464.9565781185))
      print("null deviance: {0}".format(2014.93087862689))
      print("aic: {0}".format(1494.9565781185))
  
      print("H2O:")
      print("deviance {0}".format(glm_bin_h2o.residual_deviance()))
      print("null deviance {0}".format(glm_bin_h2o.null_deviance()))
      print("aic {0}".format(glm_bin_h2o.aic()))
  
      assert (
          abs(1464.9565781185 - glm_bin_h2o.residual_deviance()) < 0.1
      )
      assert abs(2014.93087862689 - glm_bin_h2o.null_deviance()) < 0.1
      assert abs(1494.9565781185 - glm_bin_h2o.aic()) < 0.1
  
      print("Running Regression Comparisons...")
  
      glm_h2o = h2o.glm(
          x=cars_hex[2:8],
          y=cars_hex[1],
          training_frame=cars_hex,
          family="gaussian",
          standardize=False,
          offset_column="year",
          Lambda=[0],
          max_iterations=100,
      )
      print("gaussian")
      print("R:")
      print("deviance: {0}".format(4204.68399275449))
      print("null deviance: {0}".format(16072.0955102041))
      print("aic: {0}".format(2062.54330117177))
  
      print("H2O:")
      print("deviance {0}".format(glm_h2o.residual_deviance()))
      print("null deviance {0}".format(glm_h2o.null_deviance()))
      print("aic {0}".format(glm_h2o.aic()))
  
      assert abs(4204.68399275449 - glm_h2o.residual_deviance()) < 0.1
      assert abs(16072.0955102041 - glm_h2o.null_deviance()) < 0.1
      assert abs(2062.54330117177 - glm_h2o.aic()) < 0.1
  
      glm_h2o = h2o.glm(
          x=cars_hex[2:8],
          y=cars_hex[1],
          training_frame=cars_hex,
          family="poisson",
          standardize=False,
          offset_column="year",
          Lambda=[0],
          max_iterations=100,
      )
      print("poisson")
      print("R:")
      print("deviance: {0}".format(54039.1725227918))
      print("null deviance: {0}".format(59381.5624028358))
      print("aic: {0}".format("Inf"))
  
      print("H2O:")
      print("deviance {0}".format(glm_h2o.residual_deviance()))
      print("null deviance {0}".format(glm_h2o.null_deviance()))
      print("aic {0}".format(glm_h2o.aic()))
  
      assert abs(54039.1725227918 - glm_h2o.residual_deviance()) < 0.1
      assert abs(59381.5624028358 - glm_h2o.null_deviance()) < 0.1
      assert abs(float("inf") - glm_h2o.aic()) < 0.1
  
label_desc: Use image node_id 1 for calling a global function with example usage: offset_1388() without return types