code: def test_agent_input_dir(self):
      config = (
          PPOConfig()
          .environment("CartPole-v1")
          .evaluation(off_policy_estimation_methods={})
          .training(train_batch_size=250)
      )
  
      for fw in framework_iterator(config, frameworks=("torch", "tf")):
          self.write_outputs(self.test_dir, fw)
          config.offline_data(
              input_=self.test_dir + fw,
          )
          print("WROTE TO: ", self.test_dir)
          algo = config.build()
          result = algo.train()
          self.assertEqual(
              result["timesteps_total"], 250
          )  # read from input
          self.assertTrue(np.isnan(result["episode_reward_mean"]))
  
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 7 for calling the AgentIOTest obj's underlying member method code with example usage: obj.test_agent_input_dir() without return types