code: def batch_experiences(
      experiences, xp, phi, gamma, batch_states=batch_states
  ):
      """Takes a batch of k experiences each of which contains j
  
      consecutive transitions and vectorizes them, where j is between 1 and n.
  
      Args:
          experiences: list of experiences. Each experience is a list
              containing between 1 and n dicts 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
          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
      """
  
      batch_exp = {
          "state": batch_states(
              [elem[0]["state"] for elem in experiences], xp, phi
          ),
          "action": xp.asarray(
              [elem[0]["action"] for elem in experiences]
          ),
          "reward": xp.asarray(
              [
                  sum(
                      (gamma**i) * exp[i]["reward"]
                      for i in range(len(exp))
                  )
                  for exp in experiences
              ],
              dtype=np.float32,
          ),
          "next_state": batch_states(
              [elem[-1]["next_state"] for elem in experiences], xp, phi
          ),
          "is_state_terminal": xp.asarray(
              [
                  any(
                      transition["is_state_terminal"]
                      for transition in exp
                  )
                  for exp in experiences
              ],
              dtype=np.float32,
          ),
          "discount": xp.asarray(
              [(gamma ** len(elem)) for elem in experiences],
              dtype=np.float32,
          ),
      }
      if all(
          elem[-1]["next_action"] is not None for elem in experiences
      ):
          batch_exp["next_action"] = xp.asarray(
              [elem[-1]["next_action"] for elem in experiences]
          )
      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 2 for calling a global function with example usage: batch_experiences(experiences, xp, phi, gamma, batch_states) and returns: batch_exp