code: def test_agent_input_eval_sampler(self):
      config = (
          PGConfig()
          .environment("CartPole-v1")
          .offline_data(
              postprocess_inputs=True,  # adds back 'advantages'
          )
          .evaluation(
              evaluation_interval=1,
              evaluation_config=PGConfig.overrides(input_="sampler"),
          )
      )
  
      for fw in framework_iterator(config, frameworks=["tf", "torch"]):
          self.write_outputs(self.test_dir, fw)
          config.offline_data(input_=self.test_dir + fw)
          algo = config.build()
          result = algo.train()
          assert np.isnan(
              result["episode_reward_mean"]
          ), "episode reward should not be computed for offline data"
          assert not np.isnan(
              result["evaluation"]["episode_reward_mean"]
          ), "Did not see simulation results during evaluation"
          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 10 for calling the AgentIOTest obj's underlying member method code with example usage: obj.test_agent_input_eval_sampler() without return types