code: def compute_policy_gradient_sample_correction(
      action_distrib,
      action_distrib_mu,
      action_value,
      v,
      truncation_threshold,
  ):
      """Compute off-policy bias correction term wrt a sampled action."""
      assert np.isscalar(v)
      assert truncation_threshold is not None
      with chainer.no_backprop_mode():
          sample_action = action_distrib.sample().array
          rho_dash_inv = compute_importance(
              action_distrib_mu, action_distrib, sample_action
          )
          if (
              truncation_threshold > 0
              and rho_dash_inv >= 1 / truncation_threshold
          ):
              return chainer.Variable(np.asarray([0], dtype=np.float32))
          correction_weight = max(
              0, 1 - truncation_threshold * rho_dash_inv
          )
          assert correction_weight <= 1
          q = float(
              action_value.evaluate_actions(sample_action).array[0]
          )
          correction_advantage = q - v
      return -(
          correction_weight
          * action_distrib.log_prob(sample_action)
          * correction_advantage
      )
  
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 4 for calling a global function with example usage: compute_policy_gradient_sample_correction(action_distrib, action_distrib_mu, action_value, v, truncation_threshold) and returns: unknown, chainer