code: def random_attack():
      def cointoss():
          return random.randint(0, 1)
  
      def attack(family, train, valid, x, y):
          kwargs = {}
          kwargs["family"] = family
          gaussian_links = ["inverse", "log", "identity"]
          binomial_links = ["logit"]
          poisson_links = ["log", "identity"]
          gamma_links = ["inverse", "log", "identity"]
  
          # randomly select parameters and their corresponding values
          if cointoss():
              kwargs["max_iterations"] = random.randint(1, 50)
          if random.random() > 0.8:
              kwargs["beta_epsilon"] = random.random()
          if cointoss():
              kwargs["solver"] = [
                  "AUTO",
                  "IRLSM",
                  "L_BFGS",
                  "COORDINATE_DESCENT_NAIVE",
                  "COORDINATE_DESCENT",
              ][cointoss()]
          if cointoss():
              kwargs["standardize"] = [True, False][cointoss()]
          if cointoss():
              if family == "gaussian":
                  kwargs["link"] = gaussian_links[random.randint(0, 2)]
              elif family == "binomial":
                  kwargs["link"] = binomial_links[0]
              elif family == "poisson":
                  kwargs["link"] = poisson_links[cointoss()]
              elif family == "gamma":
                  kwargs["link"] = gamma_links[random.randint(0, 2)]
          if cointoss():
              kwargs["alpha"] = [random.random()]
          if family == "binomial":
              if cointoss():
                  kwargs["prior"] = random.random()
          if cointoss():
              kwargs["lambda_search"] = [True, False][cointoss()]
          if "lambda_search" in list(kwargs.keys()):
              if cointoss():
                  kwargs["nlambdas"] = random.randint(2, 10)
          do_validation = [True, False][cointoss()]
          # beta constraints
          if cointoss():
              bc = []
              for n in x:
                  if train[n].isnumeric()[0]:
                      name = train.names[n]
                      lower_bound = random.uniform(-1, 1)
                      upper_bound = lower_bound + random.random()
                      bc.append([name, lower_bound, upper_bound])
              if len(bc) > 0:
                  beta_constraints = h2o.H2OFrame(bc)
                  beta_constraints.set_names(
                      ["names", "lower_bounds", "upper_bounds"]
                  )
                  kwargs["beta_constraints"] = beta_constraints
  
          # 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 kwargs.items():
              if k == "beta_constraints":
                  print(k + ": ")
                  beta_constraints.show()
              else:
                  print(k + ": {0}".format(v))
          if do_validation:
              # h2o.glm(x=train[x], y=train[y], validation_x=valid[x], validation_y=valid[y], **kwargs)
              H2OGeneralizedLinearEstimator(**kwargs).train(
                  x=x, y=y, training_frame=train, validation_frame=valid
              )
          else:
              # h2o.glm(x=train[x], y=train[y], **kwargs)
              H2OGeneralizedLinearEstimator(**kwargs).train(
                  x=x, y=y, training_frame=train
              )
          print("-----------------------")
  
      print("Import and data munging...")
      seed = random.randint(1, 10000)
      print("SEED: {0}".format(seed))
      pros = h2o.upload_file(
          pyunit_utils.locate("smalldata/prostate/prostate.csv.zip")
      )
      pros[1] = pros[1].asfactor()
      r = pros[0].runif(
          seed=seed
      )  # 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(seed=seed)
      cars_train = cars[r > 0.2]
      cars_valid = cars[r <= 0.2]
  
      print()
      print(
          "======================================================================"
      )
      print(
          "============================== Binomial =============================="
      )
      print(
          "======================================================================"
      )
      for i in range(10):
          attack(
              "binomial",
              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(
              "gaussian",
              cars_train,
              cars_valid,
              random.sample([2, 3, 4, 5, 6, 7], random.randint(1, 6)),
              1,
          )
  
      print()
      print(
          "======================================================================"
      )
      print(
          "============================== Poisson  =============================="
      )
      print(
          "======================================================================"
      )
      for i in range(10):
          attack(
              "poisson",
              cars_train,
              cars_valid,
              random.sample([1, 3, 4, 5, 6, 7], random.randint(1, 6)),
              2,
          )
  
      print()
      print(
          "======================================================================"
      )
      print(
          "==============================  Gamma   =============================="
      )
      print(
          "======================================================================"
      )
      for i in range(10):
          attack(
              "gamma",
              pros_train,
              pros_valid,
              random.sample(
                  [1, 2, 3, 5, 6, 7, 8], random.randint(1, 7)
              ),
              4,
          )
  
imports: import h2o
  from tests import pyunit_utils
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
  import random
label_desc: Use image node_id 1 for calling a global function with example usage: random_attack() and returns: random