code: def _encode_sample(
      self,
      idxes: Union[List[int], np.ndarray],
      env: Optional[VecNormalize] = None,
  ):
      obses_t, actions, rewards, obses_tp1, dones = [], [], [], [], []
      for i in idxes:
          data = self._storage[i]
          obs_t, action, reward, obs_tp1, done = data
          obses_t.append(np.array(obs_t, copy=False))
          actions.append(np.array(action, copy=False))
          rewards.append(reward)
          obses_tp1.append(np.array(obs_tp1, copy=False))
          dones.append(done)
      return (
          self._normalize_obs(np.array(obses_t), env),
          np.array(actions),
          self._normalize_reward(np.array(rewards), env),
          self._normalize_obs(np.array(obses_tp1), env),
          np.array(dones),
      )
  
imports: import random
  from typing import Optional, List, Union
  import numpy
  from stable_baselines.common.segment_tree import SumSegmentTree, MinSegmentTree
  from stable_baselines.common.vec_env import VecNormalize
label_desc: Use image node_id 11 for calling the ReplayBuffer obj's underlying member method code with example usage: obj._encode_sample(idxes, env) and returns: self,  np,  self,  self,  np