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, torch.distributions.Categorical
              ):
                  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 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 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