class_docstr: A Loss module that computes loss for WaveGlow
      
code: def forward(self, *, z, log_s_list, log_det_W_list, sigma=1.0):
      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 * sigma * sigma)
          - log_s_total
          - log_det_W_total
      )
      return loss / (z.size(0) * z.size(1) * z.size(2))
  
file_path: NeMo/nemo/collections/tts/losses/waveglowloss.py
imports: import torch
  from nemo.core.classes import Loss, typecheck
  from nemo.core.neural_types.elements import LossType, NormalDistributionSamplesType, VoidType
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 3 for calling the WaveGlowLoss obj's underlying member method code with example usage: obj.forward() and returns: unknown