code: def random_attack():
      def attack(train, x):
          kwargs = {}
  
          # randomly select parameters and their corresponding values
          kwargs["k"] = random.randint(1, 20)
          if random.randint(0, 1):
              kwargs["model_id"] = "my_model"
          if random.randint(0, 1):
              kwargs["max_iterations"] = random.randint(1, 1000)
          if random.randint(0, 1):
              kwargs["standardize"] = [True, False][
                  random.randint(0, 1)
              ]
          if random.randint(0, 1):
              method = random.randint(0, 3)
              if method == 3:
                  # Can be simplified to: train[x].mean() + (train[x].runif() - 0.5)*200
                  # once .runif() is fixed
                  s = [
                      [
                          train[c].mean().getrow()[0]
                          + random.uniform(-100, 100)
                          for p in range(kwargs["k"])
                      ]
                      for c in x
                  ]
                  print("s: {0}".format(s))
                  start = h2o.H2OFrame(list(zip(*s)))
                  kwargs["user_points"] = start
              else:
                  kwargs["init"] = ["Furthest", "Random", "PlusPlus"][
                      method
                  ]
          if random.randint(0, 1):
              kwargs["seed"] = random.randint(1, 10000)
  
          # display the parameters and their corresponding values
          print("-----------------------")
          print("x: {0}".format(x))
          for k, v in kwargs.items():
              if k == "user_points":
                  print(k + ": ")
                  start.show()
              else:
                  print(k + ": {0}".format(v))
  
          H2OKMeansEstimator(**kwargs).train(x=x, training_frame=train)
          print("-----------------------")
  
      print("Import Ozone.csv...")
      ozone = h2o.import_file(
          path=pyunit_utils.locate("smalldata/glm_test/ozone.csv")
      )
      for i in range(50):
          attack(
              ozone, random.sample([0, 1, 2, 3], random.randint(1, 4))
          )
  
imports: import h2o
  from tests import pyunit_utils
  from h2o.estimators.kmeans import H2OKMeansEstimator
  import random
label_desc: Use image node_id 1 for calling a global function with example usage: random_attack() without return types