docstr: Verify that the infos dictionary is written to the output files.
  
  Note, with torch this is always the case.
docstr_tok: ["Verify","that","the","infos","dictionary","is","written","to","the","output","files",".","Note",",","with","torch","this","is","always","the","case","."]
code: def test_agent_output_infos(self):
      """Verify that the infos dictionary is written to the output files.
  
      Note, with torch this is always the case."""
      output_config = {"store_infos": True}
      for fw in framework_iterator(frameworks=("torch", "tf")):
          self.write_outputs(
              self.test_dir, fw, output_config=output_config
          )
          # PPO has two workers, so we expect 2 output files.
          self.assertEqual(len(os.listdir(self.test_dir + fw)), 2)
          reader = JsonReader(self.test_dir + fw + "/*.json")
          data = reader.next()
          data = convert_ma_batch_to_sample_batch(data)
          self.assertTrue("infos" in data)
  
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 6 for calling the AgentIOTest obj's underlying member method code with example usage: obj.test_agent_output_infos() without return types