code: def _add_log_prob_and_value_to_episodes_recurrent(
      episodes,
      model,
      phi,
      batch_states,
      obs_normalizer,
  ):
      xp = model.xp
  
      # Prepare data for a recurrent model
      seqs_states = []
      seqs_next_states = []
      for ep in episodes:
          states = batch_states(
              [transition["state"] for transition in ep], xp, phi
          )
          next_states = batch_states(
              [transition["next_state"] for transition in ep], xp, phi
          )
          if obs_normalizer:
              states = obs_normalizer(states, update=False)
              next_states = obs_normalizer(next_states, update=False)
          seqs_states.append(states)
          seqs_next_states.append(next_states)
  
      flat_transitions = list(itertools.chain.from_iterable(episodes))
  
      # Predict values using a recurrent model
      with chainer.using_config(
          "train", False
      ), chainer.no_backprop_mode():
          rs = model.concatenate_recurrent_states(
              [ep[0]["recurrent_state"] for ep in episodes]
          )
          next_rs = model.concatenate_recurrent_states(
              [ep[0]["next_recurrent_state"] for ep in episodes]
          )
          assert len(rs) == len(next_rs)
  
          (flat_distribs, flat_vs), _ = model.n_step_forward(
              seqs_states, recurrent_state=rs, output_mode="concat"
          )
          (_, flat_next_vs), _ = model.n_step_forward(
              seqs_next_states,
              recurrent_state=next_rs,
              output_mode="concat",
          )
  
          flat_actions = xp.array(
              [b["action"] for b in flat_transitions]
          )
          flat_log_probs = flat_distribs.log_prob(flat_actions)
          flat_log_probs = chainer.cuda.to_cpu(flat_log_probs.array)
          flat_vs = chainer.cuda.to_cpu(flat_vs.array)
          flat_next_vs = chainer.cuda.to_cpu(flat_next_vs.array)
  
      # Add predicted values to transitions
      for transition, log_prob, v, next_v in zip(
          flat_transitions, flat_log_probs, flat_vs, flat_next_vs
      ):
          transition["log_prob"] = float(log_prob)
          transition["v_pred"] = float(v)
          transition["next_v_pred"] = float(next_v)
  
imports: import collections
  import itertools
  import random
  import chainer
  from chainer import cuda
  import chainer.functions
  import numpy
  from chainerrl import agent
  from chainerrl.misc.batch_states import batch_states
label_desc: Use image node_id 5 for calling a global function with example usage: _add_log_prob_and_value_to_episodes_recurrent(episodes, model, phi, batch_states, obs_normalizer) without return types