code: def forward(
      self,
      z,
      means,
      scales,
      log_det,
      y_lengths,
      o_dur_log,
      o_attn_dur,
      x_lengths,
  ):
      return_dict = {}
      # flow loss - neg log likelihood
      pz = torch.sum(scales) + 0.5 * torch.sum(
          torch.exp(-2 * scales) * (z - means) ** 2
      )
      log_mle = self.constant_factor + (pz - torch.sum(log_det)) / (
          torch.sum(y_lengths) * z.shape[1]
      )
      # duration loss - MSE
      # loss_dur = torch.sum((o_dur_log - o_attn_dur)**2) / torch.sum(x_lengths)
      # duration loss - huber loss
      loss_dur = torch.nn.functional.smooth_l1_loss(
          o_dur_log, o_attn_dur, reduction="sum"
      ) / torch.sum(x_lengths)
      return_dict["loss"] = log_mle + loss_dur
      return_dict["log_mle"] = log_mle
      return_dict["loss_dur"] = loss_dur
  
      # check if any loss is NaN
      for key, loss in return_dict.items():
          if torch.isnan(loss):
              raise RuntimeError(f" [!] NaN loss with {key}.")
      return return_dict
  
imports: import math
  import numpy
  import torch
  from torch import nn
  from inspect import signature
  from torch.nn import functional
  from TTS.tts.utils.generic_utils import sequence_mask
  from TTS.tts.utils.ssim import ssim
label_desc: Use image node_id 2 for calling the GlowTTSLoss obj's underlying member method code with example usage: obj.forward(z, means, scales, log_det, y_lengths, o_dur_log, o_attn_dur, x_lengths) and returns: return_dict