code: def log_prior(self):
      # calculate the log prior over all the layers
      ret = 0
      for x in self.linear_bbbs:
          ret += x.log_prior
      return ret
  
imports: import logging
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from reagent.models.base import ModelBase
  from reagent.models.fully_connected_network import ACTIVATION_MAP, gaussian_fill_w_gain, SlateBatchNorm1d
  from torch.distributions import Normal
label_desc: Use image node_id 4 for calling the FullyConnectedProbabilisticNetwork obj's underlying member method code with example usage: obj.log_prior() and returns: ret