code: def javapredict_dynamic_data():
      dataset_params = {}
      dataset_params["rows"] = 13183
      dataset_params["cols"] = 13
      dataset_params["categorical_fraction"] = 0.4
      dataset_params["integer_fraction"] = 0.3
      dataset_params["missing_fraction"] = 0.27539154084819495
      dataset_params["has_response"] = True
      dataset_params["randomize"] = True
      dataset_params["factors"] = 819
      print("Dataset parameters: {0}".format(dataset_params))
  
      problem = 2
      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"] = 16
  
      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"),
      )
  
      params = {}
      params["nbins"] = 5
      params["min_rows"] = 7
      params["mtries"] = 4
      params["sample_rate"] = 0.7867986759373544
      params["seed"] = 1304644573760597606
      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: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  import os
label_desc: Use image node_id 1 for calling a global function with example usage: javapredict_dynamic_data() without return types