code: def random_attack():
      def attack(train, valid, x, y):
          kwargs = {}
  
          # randomly select parameters and their corresponding values
          if random.randint(0, 1):
              kwargs["mtries"] = random.randint(1, len(x))
          if random.randint(0, 1):
              kwargs["sample_rate"] = random.random()
          if random.randint(0, 1):
              kwargs["build_tree_one_node"] = True
          if random.randint(0, 1):
              kwargs["ntrees"] = random.randint(1, 10)
          if random.randint(0, 1):
              kwargs["max_depth"] = random.randint(1, 5)
          if random.randint(0, 1):
              kwargs["min_rows"] = random.randint(1, 10)
          if random.randint(0, 1):
              kwargs["nbins"] = random.randint(2, 20)
          if random.randint(0, 1):
              kwargs["balance_classes"] = True
              if random.randint(0, 1):
                  kwargs["max_after_balance_size"] = random.uniform(
                      0, 10
                  )
          if random.randint(0, 1):
              kwargs["seed"] = random.randint(1, 10000)
          do_validation = [True, False][random.randint(0, 1)]
  
          # display the parameters and their corresponding values
          print("-----------------------")
          print("x: {0}".format(x))
          print("y: {0}".format(y))
          print("validation: {0}".format(do_validation))
          for k, v in zip(list(kwargs.keys()), list(kwargs.values())):
              print(k + ": {0}".format(v))
          if do_validation:
              H2ORandomForestEstimator(**kwargs).train(
                  x=x, y=y, training_frame=train, validation_frame=valid
              )
              # h2o.random_forest(x=train[x], y=train[y], validation_x=valid[x], validation_y=valid[y], **kwargs)
          else:
              H2ORandomForestEstimator(**kwargs).train(
                  x=x, y=y, training_frame=train
              )
              # h2o.random_forest(x=train[x], y=train[y], **kwargs)
          print("-----------------------")
  
      print("Import and data munging...")
      pros = h2o.upload_file(
          pyunit_utils.locate("smalldata/prostate/prostate.csv.zip")
      )
      pros[1] = pros[1].asfactor()
      pros[4] = pros[4].asfactor()
      pros[5] = pros[5].asfactor()
      pros[8] = pros[8].asfactor()
      r = pros[
          0
      ].runif()  # a column of length pros.nrow with values between 0 and 1
      # ~80/20 train/validation split
      pros_train = pros[r > 0.2]
      pros_valid = pros[r <= 0.2]
  
      cars = h2o.upload_file(
          pyunit_utils.locate("smalldata/junit/cars.csv")
      )
      r = cars[0].runif()
      cars_train = cars[r > 0.2]
      cars_valid = cars[r <= 0.2]
  
      print()
      print(
          "======================================================================"
      )
      print(
          "============================== Binomial =============================="
      )
      print(
          "======================================================================"
      )
      for i in range(10):
          attack(
              pros_train,
              pros_valid,
              random.sample(
                  [2, 3, 4, 5, 6, 7, 8], random.randint(1, 7)
              ),
              1,
          )
  
      print()
      print(
          "======================================================================"
      )
      print(
          "============================== Gaussian =============================="
      )
      print(
          "======================================================================"
      )
      for i in range(10):
          attack(
              cars_train,
              cars_valid,
              random.sample([2, 3, 4, 5, 6, 7], random.randint(1, 6)),
              1,
          )
  
      print()
      print(
          "======================================================================"
      )
      print(
          "============================= Multinomial ============================"
      )
      print(
          "======================================================================"
      )
      cars_train[2] = cars_train[2].asfactor()
      cars_valid[2] = cars_valid[2].asfactor()
      for i in range(10):
          attack(
              cars_train,
              cars_valid,
              random.sample([1, 3, 4, 5, 6, 7], random.randint(1, 6)),
              2,
          )
  
imports: from builtins import zip
  from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.random_forest import H2ORandomForestEstimator
  import random
label_desc: Use image node_id 1 for calling a global function with example usage: random_attack() without return types