code: def compute_importance(pi, mu, x):
      with torch.no_grad():
          return float(torch.exp(pi.log_prob(x) - mu.log_prob(x)))
  
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 1 for calling a global function with example usage: compute_importance(pi, mu, x) and returns: float