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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def evaluate_single_config( hparams, sampling_temp, max_num_noops, agent_model_dir, eval_fn=_eval_fn_with_learner ):
 """Evaluate the PPO agent in the real environment."""

  tf.logging.info("Evaluating metric %s", get_metric_name(
      sampling_temp, max_num_noops, clipped=False
  ))
  eval_hparams = trainer_lib.create_hparams(hparams.base_algo_params)
  env = setup_env(
      hparams, batch_size=hparams.eval_batch_size, max_num_noops=max_num_noops,
      rl_env_max_episode_steps=hparams.eval_rl_env_max_episode_steps,
      env_name=hparams.rl_env_name)
  env.start_new_epoch(0)
  eval_fn(env, hparams, eval_hparams, agent_model_dir, sampling_temp)
  rollouts = env.current_epoch_rollouts()
  env.close()

  return tuple(
      compute_mean_reward(rollouts, clipped) for clipped in (True, False)
  )