code: def test_get_future_model():
      covtype = h2o.upload_file(
          pyunit_utils.locate("smalldata/covtype/covtype.altered.gz")
      )
  
      myY = 54
      myX = list(
          set(range(54)) - set([20, 28])
      )  # Cols 21 and 29 are constant, so must be explicitly ignored
  
      # Set response to be indicator of a particular class
      res_class = random.sample(list(range(1, 5)), 1)[0]
      covtype[myY] = covtype[myY] == res_class
      covtype[myY] = covtype[myY].asfactor()
  
      # L2: alpha=0, lambda=0
  
      covtype_h2o1 = H2OGeneralizedLinearEstimator(
          family="binomial", alpha=0, Lambda=0
      )
      covtype_h2o1.start(x=myX, y=myY, training_frame=covtype)
  
      # Elastic: alpha=0.5, lambda=1e-4
      covtype_h2o2 = H2OGeneralizedLinearEstimator(
          family="binomial", alpha=0.5, Lambda=1e-4
      )
      covtype_h2o2.start(x=myX, y=myY, training_frame=covtype)
  
      # L1: alpha=1, lambda=1e-4
      covtype_h2o3 = H2OGeneralizedLinearEstimator(
          family="binomial", alpha=1, Lambda=1e-4
      )
      covtype_h2o3.start(x=myX, y=myY, training_frame=covtype)
  
      covtype_h2o1.join()
      print(covtype_h2o1)
      covtype_h2o2.join()
      print(covtype_h2o2)
      covtype_h2o3.join()
      print(covtype_h2o3)
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  import random
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_get_future_model() without return types