code: def _get_ppo_module(framework, env, lstm, observation_space):
      model_config_dict = {"use_lstm": lstm}
      config = get_expected_module_config(
          env,
          model_config_dict=model_config_dict,
          observation_space=observation_space,
      )
      if framework == "torch":
          module = PPOTorchRLModule(config)
      else:
          module = PPOTfRLModule(config)
      return module
  
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 4 for calling a global function with example usage: _get_ppo_module(framework, env, lstm, observation_space) and returns: module