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 (torch.Tensor): Loss to minimize
          delta (float): Minimum KL difference
      Returns:
          torch.Tensor: new loss to minimize
      """
      distrib_params = get_params_of_distribution(distrib)
      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[0]
          for grad in torch.autograd.grad(
              [original_loss], distrib_params, retain_graph=True
          )
      ]
  
      # Compute k: a direction to increase KL div.
      kl = torch.distributions.kl_divergence(another_distrib, distrib)
      k = [
          grad[0]
          for grad in torch.autograd.grad(
              [-kl], distrib_params, retain_graph=True
          )
      ]
  
      # Compute z: combination of g and k to keep small KL div.
      kg_dot = sum(
          torch.dot(kp.flatten(), gp.flatten()) for kp, gp in zip(k, g)
      )
      kk_dot = sum(torch.dot(kp.flatten(), kp.flatten()) 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 += (p * zp).sum()
      return loss.reshape(original_loss.shape), float(kl)
  
imports: import copy
  from logging import getLogger
  import numpy
  import torch
  from torch import nn
  from pfrl import agent
  from pfrl.action_value import SingleActionValue
  from pfrl.utils import clip_l2_grad_norm_, copy_param
  from pfrl.utils.batch_states import batch_states
  from pfrl.utils.mode_of_distribution import mode_of_distribution
  from pfrl.utils.recurrent import detach_recurrent_state, one_step_forward
label_desc: Use image node_id 8 for calling a global function with example usage: compute_loss_with_kl_constraint(distrib, another_distrib, original_loss, delta) and returns: loss,  float