class_docstr: Tests some sanity checks that should pass based on the math of ope methods.
docstr: Tests the Direct Method and Doubly Robust methods.
  
  It checks whether DM and DR methods outputs are consistent when
  split_batch_by_episode is True or False (RL vs. Bandits)
docstr_tok: ["Tests","the","Direct","Method","and","Doubly","Robust","methods",".","It","checks","whether","DM","and","DR","methods","outputs","are","consistent","when","split_batch_by_episode","is","True","or","False","(","RL","vs.","Bandits",")"]
code: def test_dm_dr_math(self):
      """Tests the Direct Method and Doubly Robust methods.
  
      It checks whether DM and DR methods outputs are consistent when
      split_batch_by_episode is True or False (RL vs. Bandits)
      """
  
      ope_classes = [
          DirectMethod,
          DoublyRobust,
      ]
  
      for class_module in ope_classes:
          target_policy = self.policies["good"]
          estimator = class_module(target_policy, gamma=0)
  
          s = time.time()
          estimate_1 = estimator.estimate(
              self.sample_batch,
              split_batch_by_episode=True,
          )
          dt1 = time.time() - s
  
          s = time.time()
          estimate_2 = estimator.estimate(
              self.sample_batch, split_batch_by_episode=False
          )
          dt2 = time.time() - s
  
          # check that the estimates are the same for bandit vs RL
          check(estimate_1, estimate_2)
  
          self.assertGreater(
              dt1,
              dt2,
              f"in bandits split_by_episode = False should improve "
              f"performance, dt_wo_split={dt2}, dt_with_split={dt1}",
          )
  
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 4 for calling the TestOPEMath obj's underlying member method code with example usage: obj.test_dm_dr_math() without return types