code: def colsamplepertree():
      covtype = h2o.import_file(
          path=pyunit_utils.locate("smalldata/covtype/covtype.20k.data")
      )
      covtype[54] = covtype[54].asfactor()
  
      splits = covtype.split_frame(ratios=[0.8], seed=1234)
      train = splits[0]
      valid = splits[1]
  
      regular = H2ORandomForestEstimator(ntrees=50, seed=1234)
      regular.train(x=list(range(54)), y=54, training_frame=train)
      mm_regular = regular.model_performance(valid)
      mm_regular.show()
  
      colsample = H2ORandomForestEstimator(
          ntrees=50, seed=1234, col_sample_rate_per_tree=0.9
      )
      colsample.train(x=list(range(54)), y=54, training_frame=train)
      mm_colsample = colsample.model_performance(valid)
      mm_colsample.show()
  
      ##compare error
      err_regular = mm_regular.confusion_matrix().cell_values[7][7]
      err_colsample = mm_colsample.confusion_matrix().cell_values[7][7]
  
      print("--------------------")
      print("")
      print("err_regular")
      print(err_regular)
      print("")
      print("err_colsample")
      print(err_colsample)
      print("")
      print("--------------------")
  
      assert (
          err_regular >= 0.9 * err_colsample
      ), "col sample per tree makes it worse!"
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.random_forest import H2ORandomForestEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: colsamplepertree() without return types