code: def batch_recurrent_experiences(
      experiences, model, xp, phi, gamma, batch_states=batch_states
  ):
      """Batch experiences for recurrent model updates.
  
      Args:
          experiences: list of episodes. Each episode is a list
              containing between 1 and n dicts, each containing:
                - state (object): State
                - action (object): Action
                - reward (float): Reward
                - is_state_terminal (bool): True iff next state is terminal
                - next_state (object): Next state
          model (chainer.Link): Model that implements StatelessRecurrent.
          xp : Numpy compatible matrix library: e.g. Numpy or CuPy.
          phi : Preprocessing function
          gamma: discount factor
          batch_states: function that converts a list to a batch
      Returns:
          dict of batched transitions
      """
      flat_transitions = list(
          itertools.chain.from_iterable(experiences)
      )
      batch_exp = {
          "state": [
              batch_states(
                  [transition["state"] for transition in ep], xp, phi
              )
              for ep in experiences
          ],
          "action": xp.array(
              [transition["action"] for transition in flat_transitions]
          ),
          "reward": xp.array(
              [transition["reward"] for transition in flat_transitions],
              dtype=np.float32,
          ),
          "next_state": [
              batch_states(
                  [transition["next_state"] for transition in ep],
                  xp,
                  phi,
              )
              for ep in experiences
          ],
          "is_state_terminal": xp.array(
              [
                  transition["is_state_terminal"]
                  for transition in flat_transitions
              ],
              dtype=np.float32,
          ),
          "discount": xp.full(
              len(flat_transitions), gamma, dtype=np.float32
          ),
          "recurrent_state": model.concatenate_recurrent_states(
              [ep[0]["recurrent_state"] for ep in experiences]
          ),
          "next_recurrent_state": model.concatenate_recurrent_states(
              [ep[0]["next_recurrent_state"] for ep in experiences]
          ),
      }
      # Batch next actions only when all the transitions have them
      if all(
          transition["next_action"] is not None
          for transition in flat_transitions
      ):
          batch_exp["next_action"] = xp.asarray(
              [
                  transition["next_action"]
                  for transition in flat_transitions
              ]
          )
      return batch_exp
  
imports: from abc import ABCMeta
  from abc import abstractmethod
  from abc import abstractproperty
  import itertools
  import numpy
  from chainerrl.misc.batch_states import batch_states
label_desc: Use image node_id 3 for calling a global function with example usage: batch_recurrent_experiences(experiences, model, xp, phi, gamma, batch_states) and returns: batch_exp