code: def dummy_torch_ppo_loss(module, batch, fwd_out):
      """Dummy PPO loss function for testing purposes.
  
      Will eventually use the actual PPO loss function implemented in PPO.
  
      Args:
          batch: SampleBatch used for training.
          fwd_out: Forward output of the model.
  
      Returns:
          Loss tensor
      """
      # TODO: we should replace these components later with real ppo components when
      # RLOptimizer and RLModule are integrated together.
      # this is not exactly a ppo loss, just something to show that the
      # forward train works
      adv = batch[SampleBatch.REWARDS] - fwd_out[SampleBatch.VF_PREDS]
      action_dist_class = module.get_train_action_dist_cls()
      action_probs = action_dist_class.from_logits(
          fwd_out[SampleBatch.ACTION_DIST_INPUTS]
      ).logp(batch[SampleBatch.ACTIONS])
      actor_loss = -(action_probs * adv).mean()
      critic_loss = (adv**2).mean()
      loss = actor_loss + critic_loss
  
      return loss
  
imports: import itertools
  import unittest
  import gymnasium
  import numpy
  import tree
  import ray
  from ray.rllib import SampleBatch
  from ray.rllib.algorithms.ppo.ppo_catalog import PPOCatalog
  from ray.rllib.algorithms.ppo.tf.ppo_tf_rl_module import PPOTfRLModule
  from ray.rllib.algorithms.ppo.torch.ppo_torch_rl_module import PPOTorchRLModule
  from ray.rllib.core.models.base import STATE_IN, STATE_OUT
  from ray.rllib.core.rl_module.rl_module import RLModuleConfig
  from ray.rllib.models.preprocessors import get_preprocessor
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.torch_utils import convert_to_torch_tensor
label_desc: Use image node_id 2 for calling a global function with example usage: dummy_torch_ppo_loss(module, batch, fwd_out) and returns: loss