class_docstr: A fake policy used in test ope math to emulate a target policy that is better
  and worse than the random behavioral policy.
  
  In case of an improved policy, we assign higher probs to those actions that
  attained a higher reward and lower probs to those actions that attained a lower
  reward. We do the reverse in case of a worse policy.
code: def __init__(
      self, observation_space, action_space, sample_batch, improved=True
  ):
      self.sample_batch = sample_batch
      self.improved = improved
      self.config = {}
  
      # things that are needed for FQE Torch Model
      self.model = ...
      self.observation_space = observation_space
      self.action_space = action_space
      self.device = "cpu"
  
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 to create a new FakePolicy object from inherited base classes: TorchPolicyV2 with example: obj = FakePolicy(observation_space, action_space, sample_batch, improved)