code: def forward(self, model_output):
      z, log_s_list, log_det_W_list = model_output
      for i, log_s in enumerate(log_s_list):
          if i == 0:
              log_s_total = torch.sum(log_s)
              log_det_W_total = log_det_W_list[i]
          else:
              log_s_total = log_s_total + torch.sum(log_s)
              log_det_W_total += log_det_W_list[i]
  
      loss = (
          torch.sum(z * z) / (2 * self.sigma * self.sigma)
          - log_s_total
          - log_det_W_total
      )
      return loss / (z.size(0) * z.size(1) * z.size(2))
  
imports: import copy
  import torch
  from torch.autograd import Variable
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the WaveGlowLoss obj's underlying member method code with example usage: obj.forward(model_output) and returns: unknown