code: def compute_policy_gradient_full_correction(
      action_distrib,
      action_distrib_mu,
      action_value,
      v,
      truncation_threshold,
  ):
      """Compute off-policy bias correction term wrt all actions."""
      assert truncation_threshold is not None
      assert np.isscalar(v)
      with chainer.no_backprop_mode():
          rho_all_inv = compute_full_importance(
              action_distrib_mu, action_distrib
          )
          correction_weight = (
              np.maximum(
                  1 - truncation_threshold * rho_all_inv,
                  np.zeros_like(rho_all_inv),
              )
              * action_distrib.all_prob.array[0]
          )
          correction_advantage = action_value.q_values.array[0] - v
      return -F.sum(
          correction_weight
          * action_distrib.all_log_prob
          * correction_advantage,
          axis=1,
      )
  
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 3 for calling a global function with example usage: compute_policy_gradient_full_correction(action_distrib, action_distrib_mu, action_value, v, truncation_threshold) and returns: unknown