code: def forward(
      self,
      decoder_output,
      decoder_target,
      decoder_output_lens,
      dur_output,
      dur_target,
      input_lens,
  ):
      l1_loss = self.l1(
          decoder_output, decoder_target, decoder_output_lens
      )
      ssim_loss = self.ssim(
          decoder_output, decoder_target, decoder_output_lens
      )
      huber_loss = self.huber(dur_output, dur_target, input_lens)
      loss = l1_loss + ssim_loss + huber_loss
      return {
          "loss": loss,
          "loss_l1": l1_loss,
          "loss_ssim": ssim_loss,
          "loss_dur": huber_loss,
      }
  
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 SpeedySpeechLoss obj's underlying member method code with example usage: obj.forward(decoder_output, decoder_target, decoder_output_lens, dur_output, dur_target, input_lens) and returns: dict