code: def compute_policy_gradient_loss(
      action,
      advantage,
      action_distrib,
      action_distrib_mu,
      action_value,
      v,
      truncation_threshold,
  ):
      """Compute policy gradient loss with off-policy bias correction."""
      assert np.isscalar(advantage)
      assert np.isscalar(v)
      log_prob = action_distrib.log_prob(action)
      if action_distrib_mu is not None:
          # Off-policy
          rho = compute_importance(
              action_distrib, action_distrib_mu, action
          )
          g_loss = 0
          if truncation_threshold is None:
              g_loss -= rho * log_prob * advantage
          else:
              # Truncated off-policy policy gradient term
              g_loss -= (
                  min(truncation_threshold, rho) * log_prob * advantage
              )
              # Bias correction term
              if isinstance(
                  action_distrib, distribution.CategoricalDistribution
              ):
                  g_loss += compute_policy_gradient_full_correction(
                      action_distrib=action_distrib,
                      action_distrib_mu=action_distrib_mu,
                      action_value=action_value,
                      v=v,
                      truncation_threshold=truncation_threshold,
                  )
              else:
                  g_loss += compute_policy_gradient_sample_correction(
                      action_distrib=action_distrib,
                      action_distrib_mu=action_distrib_mu,
                      action_value=action_value,
                      v=v,
                      truncation_threshold=truncation_threshold,
                  )
      else:
          # On-policy
          g_loss = -log_prob * advantage
      return g_loss
  
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 5 for calling a global function with example usage: compute_policy_gradient_loss(action, advantage, action_distrib, action_distrib_mu, action_value, v, truncation_threshold) and returns: g_loss