text: import gym
  from ditk import logging
  from ding.model import QAC
  from ding.policy import CQLPolicy
  from ding.envs import DingEnvWrapper, BaseEnvManagerV2
  from ding.data import create_dataset
  from ding.config import compile_config
  from ding.framework import task
  from ding.framework.context import OfflineRLContext
  from ding.framework.middleware import interaction_evaluator, trainer, CkptSaver, offline_data_fetcher
  from ding.utils import set_pkg_seed
  from dizoo.classic_control.pendulum.envs.pendulum_env import PendulumEnv
  from dizoo.classic_control.pendulum.config.pendulum_cql_config import main_config, create_config
  
  
  def main():
      # If you don't have offline data, you need to prepare if first and set the data_path in config
      # For demostration, we also can train a RL policy (e.g. SAC) and collect some data
      logging.getLogger().setLevel(logging.INFO)
      cfg = compile_config(main_config, create_cfg=create_config, auto=True)
      with task.start(async_mode=False, ctx=OfflineRLContext()):
          evaluator_env = BaseEnvManagerV2(
              env_fn=[lambda: PendulumEnv(cfg.env) for _ in range(cfg.env.evaluator_env_num)], cfg=cfg.env.manager
          )
  
          set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda)
  
          dataset = create_dataset(cfg)
          model = QAC(**cfg.policy.model)
          policy = CQLPolicy(cfg.policy, model=model)
  
          task.use(interaction_evaluator(cfg, policy.eval_mode, evaluator_env))
          task.use(offline_data_fetcher(cfg, dataset))
          task.use(trainer(cfg, policy.learn_mode))
          task.use(CkptSaver(cfg, policy, train_freq=100))
          task.run()
  
  
  if __name__ == "__main__":
      main()
  