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))
  
      append_response = False
      distribution = random.sample(
          ["bernoulli", "multinomial", "gaussian", "poisson", "gamma"],
          1,
      )[0]
      if distribution == "bernoulli":
          dataset_params["response_factors"] = 2
      elif distribution == "gaussian":
          dataset_params["response_factors"] = 1
      elif distribution == "multinomial":
          dataset_params["response_factors"] = random.randint(3, 100)
      else:
          dataset_params["has_response"] = False
          response = h2o.H2OFrame(
              [
                  [random.randint(1, 1000)]
                  for r in range(0, dataset_params["rows"])
              ]
          )
          append_response = True
      print("Distribution: {0}".format(distribution))
  
      train = h2o.create_frame(**dataset_params)
      if append_response:
          train = response.cbind(train)
          train.set_name(0, "response")
      if distribution == "bernoulli" or distribution == "multinomial":
          train["response"] = train["response"].asfactor()
      results_dir = pyunit_utils.locate("results")
      h2o.download_csv(
          train["response"],
          os.path.join(
              results_dir, "dl_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, "dl_dynamic_training_dataset.log"),
      )
  
      # Generate random parameters
      params = {}
      if random.randint(0, 1):
          params["activation"] = random.sample(
              [
                  "Rectifier",
                  "Tanh",
                  "TanhWithDropout",
                  "RectifierWithDropout",
                  "MaxoutWithDropout",
              ],
              1,
          )[0]
      if random.randint(0, 1):
          params["epochs"] = random.sample(list(range(1, 10)), 1)[0]
      if random.randint(0, 1):
          h = random.randint(10, 21)
          params["hidden"] = [h for x in range(random.randint(2, 3))]
      params["distribution"] = distribution
      params["l1"] = random.random()
      print("Parameter list: {0}".format(params))
  
      x = train.names
      x.remove("response")
      y = "response"
  
      pyunit_utils.javapredict(
          algo="deeplearning",
          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