class_docstr: Tests some sanity checks that should pass based on the math of ope methods.
code: def setUpClass(cls):
      ray.init()
  
      bsize = 1024
      action_dim = 2
      observation_space = gym.spaces.Box(
          -float("inf"), float("inf"), (1,)
      )
      action_space = gym.spaces.Discrete(action_dim)
      cls.sample_batch = SampleBatch(
          {
              SampleBatch.OBS: np.arange(bsize).reshape(-1, 1),
              SampleBatch.NEXT_OBS: np.arange(bsize).reshape(-1, 1) + 1,
              SampleBatch.ACTIONS: np.random.randint(
                  0, action_dim, size=bsize
              ),
              SampleBatch.REWARDS: np.random.randint(
                  1, 3, size=bsize
              ),  # rewards are 1 or 2
              SampleBatch.TERMINATEDS: np.ones(bsize),
              SampleBatch.TRUNCATEDS: np.zeros(bsize),
              SampleBatch.EPS_ID: np.arange(bsize),
              SampleBatch.ACTION_PROB: np.ones(bsize) / action_dim,
          }
      )
  
      cls.policies = {
          "good": FakePolicy(
              observation_space,
              action_space,
              cls.sample_batch,
              improved=True,
          ),
          "bad": FakePolicy(
              observation_space,
              action_space,
              cls.sample_batch,
              improved=False,
          ),
      }
  
imports: import unittest
  import time
  import gymnasium
  import torch
  import numpy
  from ray.rllib.offline.estimators import DirectMethod, DoublyRobust, ImportanceSampling, WeightedImportanceSampling
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
  from ray.rllib.utils.test_utils import check
  import ray
label_desc: Use image node_id 1 for calling the TestOPEMath obj's underlying member method code with example usage: obj.setUpClass(cls) without return types