code: def compute_full_importance(pi, mu):
      assert isinstance(pi, torch.distributions.Categorical)
      assert isinstance(mu, torch.distributions.Categorical)
      # Categorical.logits is already normalized, i.e., exp(logits[i]) = probs[i]
      with torch.no_grad():
          pimu = torch.exp(pi.logits - mu.logits)
      return pimu
  
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 2 for calling a global function with example usage: compute_full_importance(pi, mu) and returns: pimu