code: def compute_loss_with_kl_constraint(
      distrib, another_distrib, original_loss, delta
  ):
      """Compute loss considering a KL constraint.
  
      Args:
          distrib (Distribution): Distribution to optimize
          another_distrib (Distribution): Distribution used to compute KL
          original_loss (chainer.Variable): Loss to minimize
          delta (float): Minimum KL difference
      Returns:
          loss (chainer.Variable)
      """
      for param in distrib.params:
          assert param.shape[0] == 1
          assert param.requires_grad
      # Compute g: a direction to minimize the original loss
      g = [
          grad.array[0]
          for grad in chainer.grad(
              [F.squeeze(original_loss)], distrib.params
          )
      ]
  
      # Compute k: a direction to increase KL div.
      kl = F.squeeze(another_distrib.kl(distrib))
      k = [
          grad.array[0] for grad in chainer.grad([-kl], distrib.params)
      ]
  
      # Compute z: combination of g and k to keep small KL div.
      kg_dot = sum(
          np.dot(kp.ravel(), gp.ravel()) for kp, gp in zip(k, g)
      )
      kk_dot = sum(np.dot(kp.ravel(), kp.ravel()) for kp in k)
      if kk_dot > 0:
          k_factor = max(0, ((kg_dot - delta) / kk_dot))
      else:
          k_factor = 0
      z = [gp - k_factor * kp for kp, gp in zip(k, g)]
      loss = 0
      for p, zp in zip(distrib.params, z):
          loss += F.sum(p * zp)
      return F.reshape(loss, original_loss.shape), float(kl.array)
  
imports: import copy
  from logging import getLogger
  import chainer
  from chainer import functions
  import numpy
  from chainerrl.action_value import SingleActionValue
  from chainerrl import agent
  from chainerrl import distribution
  from chainerrl import links
  from chainerrl.misc import async_
  from chainerrl.misc import copy_param
  from chainerrl.recurrent import Recurrent
  from chainerrl.recurrent import RecurrentChainMixin
  from chainerrl.recurrent import state_kept
  from chainerrl.recurrent import state_reset
label_desc: Use image node_id 6 for calling a global function with example usage: compute_loss_with_kl_constraint(distrib, another_distrib, original_loss, delta) and returns: F,  float