code: def test_bias_correction(self):
      if self.distrib_type == "Gaussian":
          base_policy = chainerrl.policies.FCGaussianPolicy(
              1,
              self.action_size,
              n_hidden_channels=0,
              n_hidden_layers=0,
          )
          another_policy = chainerrl.policies.FCGaussianPolicy(
              1,
              self.action_size,
              n_hidden_channels=0,
              n_hidden_layers=0,
          )
      elif self.distrib_type == "Softmax":
          base_policy = chainerrl.policies.FCSoftmaxPolicy(
              1, self.n_actions, n_hidden_channels=0, n_hidden_layers=0
          )
          another_policy = chainerrl.policies.FCSoftmaxPolicy(
              1, self.n_actions, n_hidden_channels=0, n_hidden_layers=0
          )
      x = np.full((1, 1), 1, dtype=np.float32)
      pi = base_policy(x)
      mu = another_policy(x)
  
      def evaluate_action(action):
          return float(action_value.evaluate_actions(action).array)
  
      if self.distrib_type == "Gaussian":
          W = np.random.rand(self.action_size, 1).astype(np.float32)
          action_value = chainerrl.action_value.SingleActionValue(
              evaluator=lambda x: chainer.Variable(
                  np.asarray(np.dot(x, W), dtype=np.float32)
              )
          )
      else:
          q_values = np.zeros((1, self.n_actions), dtype=np.float32)
          q_values[:, np.random.randint(self.n_actions)] = 1
          action_value = chainerrl.action_value.DiscreteActionValue(
              chainer.Variable(q_values)
          )
  
      n = 1000
  
      pi_samples = [pi.sample().array for _ in range(n)]
      mu_samples = [mu.sample().array for _ in range(n)]
  
      onpolicy_gs = []
      for sample in pi_samples:
          base_policy.cleargrads()
          loss = -evaluate_action(sample) * pi.log_prob(sample)
          F.squeeze(loss).backward()
          onpolicy_gs.append(
              extract_gradients_as_single_vector(base_policy)
          )
      # on-policy
      onpolicy_gs_mean = np.mean(onpolicy_gs, axis=0)
      onpolicy_gs_var = np.var(onpolicy_gs, axis=0)
      print("on-policy")
      print("g mean", onpolicy_gs_mean)
      print("g var", onpolicy_gs_var)
  
      # off-policy without importance sampling
      offpolicy_gs = []
      for sample in mu_samples:
          base_policy.cleargrads()
          loss = -evaluate_action(sample) * pi.log_prob(sample)
          F.squeeze(loss).backward()
          offpolicy_gs.append(
              extract_gradients_as_single_vector(base_policy)
          )
      offpolicy_gs_mean = np.mean(offpolicy_gs, axis=0)
      offpolicy_gs_var = np.var(offpolicy_gs, axis=0)
      print("off-policy")
      print("g mean", offpolicy_gs_mean)
      print("g var", offpolicy_gs_var)
  
      # off-policy with importance sampling
      is_gs = []
      for sample in mu_samples:
          base_policy.cleargrads()
          rho = float(pi.prob(sample).array / mu.prob(sample).array)
          loss = -rho * evaluate_action(sample) * pi.log_prob(sample)
          F.squeeze(loss).backward()
          is_gs.append(extract_gradients_as_single_vector(base_policy))
      is_gs_mean = np.mean(is_gs, axis=0)
      is_gs_var = np.var(is_gs, axis=0)
      print("importance sampling")
      print("g mean", is_gs_mean)
      print("g var", is_gs_var)
  
      # off-policy with truncated importance sampling + bias correction
      def bias_correction_policy_gradients(truncation_threshold):
          gs = []
          for sample in mu_samples:
              base_policy.cleargrads()
              loss = acer.compute_policy_gradient_loss(
                  action=sample,
                  advantage=evaluate_action(sample),
                  action_distrib=pi,
                  action_distrib_mu=mu,
                  action_value=action_value,
                  v=0,
                  truncation_threshold=truncation_threshold,
              )
              F.squeeze(loss).backward()
              gs.append(extract_gradients_as_single_vector(base_policy))
          return gs
  
      # c=0 means on-policy sampling
      print("truncated importance sampling + bias correction c=0")
      tis_c0_gs = bias_correction_policy_gradients(0)
      tis_c0_gs_mean = np.mean(tis_c0_gs, axis=0)
      tis_c0_gs_var = np.var(tis_c0_gs, axis=0)
      print("g mean", tis_c0_gs_mean)
      print("g var", tis_c0_gs_var)
      # c=0 must be low-bias compared to naive off-policy sampling
      self.assertLessEqual(
          np.linalg.norm(onpolicy_gs_mean - tis_c0_gs_mean),
          np.linalg.norm(onpolicy_gs_mean - offpolicy_gs_mean),
      )
  
      # c=1 means truncated importance sampling with bias correction
      print("truncated importance sampling + bias correction c=1")
      tis_c1_gs = bias_correction_policy_gradients(1)
      tis_c1_gs_mean = np.mean(tis_c1_gs, axis=0)
      tis_c1_gs_var = np.var(tis_c1_gs, axis=0)
      print("g mean", tis_c1_gs_mean)
      print("g var", tis_c1_gs_var)
      # c=1 must be low-variance compared to naive importance sampling
      self.assertLessEqual(tis_c1_gs_var.sum(), is_gs_var.sum())
      # c=1 must be low-bias compared to naive off-policy sampling
      self.assertLess(
          np.linalg.norm(onpolicy_gs_mean - tis_c1_gs_mean),
          np.linalg.norm(onpolicy_gs_mean - offpolicy_gs_mean),
      )
  
      # c=inf means importance sampling no truncation
      print("truncated importance sampling + bias correction c=inf")
      tis_cinf_gs = bias_correction_policy_gradients(np.inf)
      tis_cinf_gs_mean = np.mean(tis_cinf_gs, axis=0)
      tis_cinf_gs_var = np.var(tis_cinf_gs, axis=0)
      print("g mean", tis_cinf_gs_mean)
      print("g var", tis_cinf_gs_var)
      np.testing.assert_allclose(
          tis_cinf_gs_mean, is_gs_mean, rtol=1e-3
      )
      np.testing.assert_allclose(tis_cinf_gs_var, is_gs_var, rtol=1e-3)
  
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 TestBiasCorrection obj's underlying member method code with example usage: obj.test_bias_correction() and returns: float, gs