code: def covtype_get_model():
      covtype = h2o.import_file(
          path=pyunit_utils.locate("smalldata/covtype/covtype.20k.data")
      )
  
      Y = 54
      X = list(range(0, 20)) + list(range(29, 54))
  
      # Set response to be indicator of a particular class
      res_class = random.randint(1, 4)
      # Log.info(paste("Setting response column", myY, "to be indicator of class", res_class, "\n"))
      covtype[54] = covtype[54] == res_class
  
      # L2: alpha = 0, lambda = 0
  
      covtype_mod1 = H2OGeneralizedLinearEstimator(
          family="binomial", alpha=0, Lambda=0
      )
      covtype_mod1.train(x=X, y=Y, training_frame=covtype)
      covtype_mod1.show()
      covtype_mod1 = h2o.get_model(covtype_mod1.model_id)
      covtype_mod1.show()
  
      # Elastic: alpha = 0.5, lambda = 1e-4
      covtype_mod2 = H2OGeneralizedLinearEstimator(
          family="binomial", alpha=0.5, Lambda=1e-4
      )
      covtype_mod2.train(x=X, y=Y, training_frame=covtype)
      covtype_mod2.show()
      covtype_mod2 = h2o.get_model(covtype_mod2.model_id)
      covtype_mod2.show()
  
      # L1: alpha = 1, lambda = 1e-4
      covtype_mod3 = H2OGeneralizedLinearEstimator(
          family="binomial", alpha=1, Lambda=1e-4
      )
      covtype_mod3.train(x=X, y=Y, training_frame=covtype)
      covtype_mod3.show()
      covtype_mod3 = h2o.get_model(covtype_mod3.model_id)
      covtype_mod3.show()
  
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: covtype_get_model() without return types