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 compute_log_likelihoods(
      self,
      actions,
      obs_batch,
      *args,
      **kwargs,
  ):
      # used in IS and WIS
      inds = obs_batch[:, 0]
      old_probs = self.sample_batch[SampleBatch.ACTION_PROB][inds]
      old_rewards = self.sample_batch[SampleBatch.REWARDS][inds]
  
      if self.improved:
          # assign 50 percent higher prob to those that gave a good reward and 50
          # percent lower prob to those that gave a bad reward
          # rewards are 1 or 2 in this case
          new_probs = (old_rewards == 2) * 1.5 * old_probs + (
              old_rewards == 1
          ) * 0.5 * old_probs
      else:
          new_probs = (old_rewards == 2) * 0.5 * old_probs + (
              old_rewards == 1
          ) * 1.5 * old_probs
  
      return np.log(new_probs)
  
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 3 for calling the FakePolicy obj's underlying member method code with example usage: obj.compute_log_likelihoods(actions, obs_batch) and returns: np