code: def _add_log_prob_and_value_to_episodes(
      episodes,
      model,
      phi,
      batch_states,
      obs_normalizer,
  ):
      dataset = list(itertools.chain.from_iterable(episodes))
      xp = model.xp
  
      # Compute v_pred and next_v_pred
      states = batch_states([b["state"] for b in dataset], xp, phi)
      next_states = batch_states(
          [b["next_state"] for b in dataset], xp, phi
      )
  
      if obs_normalizer:
          states = obs_normalizer(states, update=False)
          next_states = obs_normalizer(next_states, update=False)
  
      with chainer.using_config(
          "train", False
      ), chainer.no_backprop_mode():
          distribs, vs_pred = model(states)
          _, next_vs_pred = model(next_states)
  
          actions = xp.array([b["action"] for b in dataset])
          log_probs = chainer.cuda.to_cpu(
              distribs.log_prob(actions).array
          )
          vs_pred = chainer.cuda.to_cpu(vs_pred.array.ravel())
          next_vs_pred = chainer.cuda.to_cpu(next_vs_pred.array.ravel())
  
      for transition, log_prob, v_pred, next_v_pred in zip(
          dataset, log_probs, vs_pred, next_vs_pred
      ):
          transition["log_prob"] = log_prob
          transition["v_pred"] = v_pred
          transition["next_v_pred"] = next_v_pred
  
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 6 for calling a global function with example usage: _add_log_prob_and_value_to_episodes(episodes, model, phi, batch_states, obs_normalizer) without return types