code: def test_multiple_output_workers(self):
      ray.shutdown()
      ray.init(num_cpus=4, ignore_reinit_error=True)
  
      config = (
          PPOConfig()
          .environment("CartPole-v1")
          .rollouts(num_rollout_workers=2)
          .training(train_batch_size=500)
          .evaluation(off_policy_estimation_methods={})
      )
  
      for fw in framework_iterator(config, frameworks=["tf", "torch"]):
          config.offline_data(output=self.test_dir + fw)
          algo = config.build()
          algo.train()
          self.assertEqual(len(os.listdir(self.test_dir + fw)), 2)
          reader = JsonReader(self.test_dir + fw + "/*.json")
          reader.next()
          algo.stop()
  
imports: import glob
  import json
  import numpy
  import os
  import random
  import shutil
  import tempfile
  import unittest
  import ray
  from ray.tune.registry import register_env, register_input, registry_get_input, registry_contains_input
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.algorithms.pg import PGConfig
  from ray.rllib.algorithms.ppo import PPOConfig
  from ray.rllib.examples.env.multi_agent import MultiAgentCartPole
  from ray.rllib.offline import IOContext, JsonWriter, JsonReader, InputReader, ShuffledInput, DatasetWriter
  from ray.rllib.offline.json_reader import from_json_data
  from ray.rllib.offline.json_writer import _to_json_dict, _to_json
  from ray.rllib.policy.sample_batch import SampleBatch, convert_ma_batch_to_sample_batch
  from ray.rllib.utils.test_utils import framework_iterator
label_desc: Use image node_id 15 for calling the AgentIOTest obj's underlying member method code with example usage: obj.test_multiple_output_workers() without return types