code: def forward(self, spect_predicted, spect_tgt):
      spect_tgt.requires_grad = False
      spect_tgt = spect_tgt.transpose(1, 2)  # (B, T, H)
      spect_predicted = spect_predicted.transpose(1, 2)  # (B, T, H)
  
      ldiff = spect_tgt.size(1) - spect_predicted.size(1)
      spect_predicted = F.pad(
          spect_predicted, (0, 0, 0, ldiff, 0, 0), value=0.0
      )
      mel_mask = spect_tgt.ne(0).float()
      loss_fn = F.mse_loss
      mel_loss = loss_fn(spect_predicted, spect_tgt, reduction="none")
      mel_loss = (mel_loss * mel_mask).sum() / mel_mask.sum()
  
      return mel_loss
  
imports: import torch
  import torch.nn.functional
  from nemo.collections.tts.modules.transformer import mask_from_lens
  from nemo.core.classes import Loss, typecheck
  from nemo.core.neural_types.elements import LengthsType, LossType, MelSpectrogramType, RegressionValuesType, TokenDurationType, TokenLogDurationType
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 3 for calling the MelLoss obj's underlying member method code with example usage: obj.forward(spect_predicted, spect_tgt) and returns: mel_loss