code: def __init__(
      self,
      sess,
      ob_space,
      ac_space,
      n_env,
      n_steps,
      n_batch,
      n_lstm=64,
      reuse=False,
      **_kwargs,
  ):
      super().__init__(
          sess,
          ob_space,
          ac_space,
          n_env,
          n_steps,
          n_batch,
          n_lstm,
          reuse,
          net_arch=[8, "lstm", dict(vf=[5, 10], pi=[10])],
          layer_norm=True,
          feature_extraction="mlp",
          **_kwargs,
      )
  
imports: import os
  from gym.envs.classic_control import CartPoleEnv
  from gym.wrappers.time_limit import TimeLimit
  from gym import spaces
  import numpy
  import pytest
  from stable_baselines import A2C, ACER, ACKTR, PPO2, bench
  from stable_baselines.common.policies import MlpLstmPolicy, LstmPolicy
  from stable_baselines.common.vec_env import SubprocVecEnv
  from stable_baselines.common.vec_env.vec_normalize import VecNormalize
  from stable_baselines.common.math_util import safe_mean
  from stable_baselines.common.evaluation import evaluate_policy
label_desc: Use image node_id 1 to create a new CustomLSTMPolicy4 object from inherited base classes: LstmPolicy with example: obj = CustomLSTMPolicy4(sess, ob_space, ac_space, n_env, n_steps, n_batch, n_lstm, reuse)