code: def get_model_config(framework, lstm=False):
      return (
          dict(
              use_lstm=True,
              lstm_use_prev_action=True,
              lstm_use_prev_reward=True,
              lstm_cell_size=10,
              max_seq_len=20,
          )
          if lstm
          else {"use_lstm": False}
      )
  
file_path: ray/python/ray/rllib/algorithms/ppo/tests/test_ppo_with_env_runner.py
imports: import unittest
  import ray
  import ray.rllib.algorithms.ppo
  from ray.rllib.algorithms.ppo.ppo_learner import LEARNER_RESULTS_CURR_ENTROPY_COEFF_KEY
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  from ray.rllib.core.learner.learner import LEARNER_RESULTS_CURR_LR_KEY
  from ray.rllib.env.single_agent_env_runner import SingleAgentEnvRunner
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
  from ray.rllib.utils.metrics.learner_info import LEARNER_INFO
  from ray.rllib.utils.test_utils import check, check_train_results, framework_iterator
label_desc: Use image node_id 1 for calling a global function with example usage: get_model_config(framework, lstm) and returns: unknown