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 action_distribution_fn(self, model, obs_batch=None, **kwargs):
      # used in DM and DR (FQE torch model to be precise)
      dist_class = TorchCategorical
  
      inds = obs_batch[SampleBatch.OBS][:, 0]
  
      old_rewards = self.sample_batch[SampleBatch.REWARDS][inds]
      old_actions = self.sample_batch[SampleBatch.ACTIONS][inds]
  
      dist_inputs = torch.ones(
          (len(inds), self.action_space.n), dtype=torch.float32
      )
  
      # add 0.5 to the action that gave a good reward (2) and subtract 0.5 from the
      # action that gave a bad reward (1)
      # to acheive this I can just subtract 1.5 from old_reward
      delta = old_rewards - 1.5
      if not self.improved:
          # reverse the logic for a worse policy
          delta = -delta
      dist_inputs[torch.arange(len(inds)), old_actions] = (
          dist_inputs[torch.arange(len(inds)), old_actions] + delta
      ).float()
  
      return dist_inputs, dist_class, None
  
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 2 for calling the FakePolicy obj's underlying member method code with example usage: obj.action_distribution_fn(model, obs_batch) and returns: dist_inputs,  dist_class,  None