code: def javapredict_dynamic_data():
      # Generate random dataset
      dataset_params = {}
      dataset_params["rows"] = random.sample(
          list(range(5000, 15001)), 1
      )[0]
      dataset_params["cols"] = random.sample(list(range(10, 21)), 1)[0]
      dataset_params["categorical_fraction"] = round(random.random(), 1)
      left_over = 1 - dataset_params["categorical_fraction"]
      dataset_params["integer_fraction"] = round(
          left_over - round(random.uniform(0, left_over), 1), 1
      )
      if (
          dataset_params["integer_fraction"]
          + dataset_params["categorical_fraction"]
          == 1
      ):
          if (
              dataset_params["integer_fraction"]
              > dataset_params["categorical_fraction"]
          ):
              dataset_params["integer_fraction"] = (
                  dataset_params["integer_fraction"] - 0.1
              )
          else:
              dataset_params["categorical_fraction"] = (
                  dataset_params["categorical_fraction"] - 0.1
              )
      dataset_params["missing_fraction"] = random.uniform(0, 0.5)
      dataset_params["has_response"] = True
      dataset_params["randomize"] = True
      dataset_params["factors"] = random.randint(2, 2000)
      print("Dataset parameters: {0}".format(dataset_params))
  
      problem = random.sample(list(range(0, 3)), 1)
      print(
          "Model-building exercise (0:regression, 1:binomial, 2:multinomial): {0}".format(
              problem
          )
      )
      if problem == "binomial":
          dataset_params["response_factors"] = 2
      elif problem == "regression":
          dataset_params["response_factors"] = 1
      else:
          dataset_params["response_factors"] = random.randint(3, 100)
  
      train = h2o.create_frame(**dataset_params)
      if problem == "binomial" or problem == "multinomial":
          train["response"] = train["response"].asfactor()
      results_dir = pyunit_utils.locate("results")
      h2o.download_csv(
          train["response"],
          os.path.join(
              results_dir, "drf_dynamic_preimputed_response.log"
          ),
      )
      train.impute("response", method="mode")
      print("Training dataset:")
      print(train)
  
      # Save dataset to results directory
      h2o.download_csv(
          train,
          os.path.join(results_dir, "drf_dynamic_training_dataset.log"),
      )
  
      # Generate random parameters
      params = {}
      if random.randint(0, 1):
          params["ntrees"] = random.sample(list(range(1, 21)), 1)[0]
      if random.randint(0, 1):
          params["max_depth"] = random.sample(list(range(1, 11)), 1)[0]
      if random.randint(0, 1):
          params["min_rows"] = random.sample(list(range(1, 11)), 1)[0]
      if random.randint(0, 1):
          params["nbins"] = random.sample(list(range(2, 21)), 1)[0]
      if random.randint(0, 1):
          params["nbins_cats"] = random.sample(list(range(2, 1025)), 1)[
              0
          ]
      if random.randint(0, 1):
          params["mtries"] = random.sample(
              list(range(1, dataset_params["cols"] + 1)), 1
          )[0]
      if random.randint(0, 1):
          params["sample_rate"] = random.random()
      print("Parameter list: {0}".format(params))
  
      x = list(range(1, train.ncol))
      y = "response"
  
      pyunit_utils.javapredict(
          algo="random_forest",
          equality=None,
          train=train,
          test=None,
          x=x,
          y=y,
          compile_only=True,
          **params,
      )
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  import random
  import os
label_desc: Use image node_id 1 for calling a global function with example usage: javapredict_dynamic_data() without return types