code: def test_compute_loss_with_kl_constraint(self):
      if self.distrib_type == "Gaussian":
          base_policy = chainerrl.policies.FCGaussianPolicy(
              1, 3, n_hidden_channels=0, n_hidden_layers=0
          )
          x = np.random.rand(1, 1).astype(np.float32, copy=False)
      elif self.distrib_type == "Softmax":
          base_policy = chainerrl.policies.FCSoftmaxPolicy(
              1, 3, n_hidden_channels=0, n_hidden_layers=0
          )
          x = np.random.rand(1, 1).astype(np.float32, copy=False)
      base_distrib = base_policy(x)
      sample = base_distrib.sample().array
      another_distrib = base_policy(
          np.random.rand(1, 1).astype(np.float32, copy=False)
      ).copy()
  
      def base_loss_func(distrib):
          return distrib.log_prob(sample)
  
      kl_before = float(another_distrib.kl(base_distrib).array)
      print("kl_before", kl_before)
  
      def compute_kl_after_update(loss_func, n=100):
          policy = copy.deepcopy(base_policy)
          optimizer = chainer.optimizers.SGD(1e-4)
          optimizer.setup(policy)
          for _ in range(n):
              distrib = policy(x)
              policy.cleargrads()
              F.squeeze(loss_func(distrib)).backward()
              optimizer.update()
          distrib_after = policy(x)
          return float(another_distrib.kl(distrib_after).array)
  
      # Without kl constraint
      kl_after_without_constraint = compute_kl_after_update(
          base_loss_func
      )
      print("kl_after_without_constraint", kl_after_without_constraint)
  
      # With kl constraint
      def loss_func_with_constraint(distrib):
          loss, kl = acer.compute_loss_with_kl_constraint(
              distrib, another_distrib, base_loss_func(distrib), delta=0
          )
          return loss
  
      kl_after_with_constraint = compute_kl_after_update(
          loss_func_with_constraint
      )
      print("kl_after_with_constraint", kl_after_with_constraint)
  
imports: import copy
  import logging
  import os
  import tempfile
  import unittest
  import warnings
  import chainer
  from chainer import functions
  from chainer import links
  from chainer import testing
  from chainer.testing import condition
  import numpy
  import chainerrl
  from chainerrl.agents import acer
  from chainerrl.envs.abc import ABC
  from chainerrl.experiments.train_agent_async import train_agent_async
  from chainerrl.optimizers import rmsprop_async
  from chainerrl import policies
  from chainerrl import q_function
  from chainerrl.replay_buffer import EpisodicReplayBuffer
  from chainerrl import v_function
label_desc: Use image node_id 2 for calling the TestEfficientTRPO obj's underlying member method code with example usage: obj.test_compute_loss_with_kl_constraint() and returns: distrib, float, loss