class_docstr: Compilation tests for using OPE both standalone and in an RLlib Algorithm
class_docstr_tok: ["Compilation","tests","for","using","OPE","both","standalone","and","in","an","RLlib","Algorithm"]
code: def test_algo_with_ope_from_checkpoint(self):
      algo = self.config_dqn_on_cartpole.build()
      tmpdir = tempfile.mkdtemp()
      algo.save_checkpoint(tmpdir)
      algo = Algorithm.from_checkpoint(tmpdir)
      shutil.rmtree(tmpdir)
  
imports: from typing import TYPE_CHECKING, Tuple
  import copy
  import gymnasium
  import numpy
  import os
  import shutil
  import tempfile
  import pandas
  from pathlib import Path
  import unittest
  import ray
  from ray.data import read_json
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.dqn import DQNConfig
  from ray.rllib.examples.env.cliff_walking_wall_env import CliffWalkingWallEnv
  from ray.rllib.examples.policy.cliff_walking_wall_policy import CliffWalkingWallPolicy
  from ray.rllib.offline.dataset_reader import DatasetReader
  from ray.rllib.offline.estimators import DirectMethod, DoublyRobust, ImportanceSampling, WeightedImportanceSampling
  from ray.rllib.offline.estimators.fqe_torch_model import FQETorchModel
  from ray.rllib.policy.sample_batch import SampleBatch, concat_samples
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.test_utils import check
label_desc: Use image node_id 8 for calling the TestOPE obj's underlying member method code with example usage: obj.test_algo_with_ope_from_checkpoint() without return types