code: def forward(self, log_durs_predicted, durs_tgt, len):
      dur_mask = mask_from_lens(len, max_len=durs_tgt.size(1))
      log_durs_tgt = torch.log(durs_tgt.float() + 1)
      loss_fn = F.mse_loss
      dur_loss = loss_fn(
          log_durs_predicted, log_durs_tgt, reduction="none"
      )
      dur_loss = (dur_loss * dur_mask).sum() / dur_mask.sum()
      dur_loss *= self.loss_scale
  
      return dur_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 4 for calling the DurationLoss obj's underlying member method code with example usage: obj.forward(log_durs_predicted, durs_tgt, len) and returns: dur_loss