code: def test_agent_input_postprocessing_enabled(self):
      config = (
          PGConfig()
          .environment("CartPole-v1")
          .offline_data(
              postprocess_inputs=True,  # adds back 'advantages'
          )
          .evaluation(off_policy_estimation_methods={})
      )
  
      for fw in framework_iterator(config, frameworks=("tf", "torch")):
          self.write_outputs(self.test_dir, fw)
          config.offline_data(input_=self.test_dir + fw)
  
          # Rewrite the files to drop advantages and value_targets for
          # testing
          for path in glob.glob(self.test_dir + fw + "/*.json"):
              out = []
              with open(path) as f:
                  for line in f.readlines():
                      data_string = json.loads(line)
                      data = from_json_data(data_string, None)
                      data = convert_ma_batch_to_sample_batch(data)
                      # Data won't contain rewards as these are not included
                      # in the write_outputs run (not needed in the
                      # SampleBatch). Flip out "rewards" for "advantages"
                      # just for testing.
                      data["rewards"] = data["advantages"]
                      del data["advantages"]
                      if "value_targets" in data:
                          del data["value_targets"]
                      out.append(_to_json_dict(data, []))
              with open(path, "w") as f:
                  for data in out:
                      f.write(json.dumps(data))
  
          algo = config.build()
          result = algo.train()
          self.assertEqual(
              result["timesteps_total"], 250
          )  # read from input
          self.assertTrue(np.isnan(result["episode_reward_mean"]))
          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 9 for calling the AgentIOTest obj's underlying member method code with example usage: obj.test_agent_input_postprocessing_enabled() without return types