class_docstr: Loss function module for duration predictor.
  
  The loss value is Calculated in log domain to make it Gaussian.
docstr: Calculate forward propagation.
  
  Args:
      outputs (Tensor): Batch of prediction durations in log domain (B, T)
      targets (LongTensor): Batch of groundtruth durations in linear domain (B, T)
  
  Returns:
      Tensor: Mean squared error loss value.
  
  Note:
      `outputs` is in log domain but `targets` is in linear domain.
code: def forward(self, outputs, targets):
      """Calculate forward propagation.
  
      Args:
          outputs (Tensor): Batch of prediction durations in log domain (B, T)
          targets (LongTensor): Batch of groundtruth durations in linear domain (B, T)
  
      Returns:
          Tensor: Mean squared error loss value.
  
      Note:
          `outputs` is in log domain but `targets` is in linear domain.
  
      """
      # NOTE: outputs is in log domain while targets in linear
      targets = torch.log(targets.float() + self.offset)
      loss = self.criterion(outputs, targets)
  
      return loss
  
file_path: espnet/espnet/nets/pytorch_backend/fastspeech/duration_predictor.py
imports: import torch
  from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
label_desc: Use image node_id 2 for calling the DurationPredictorLoss obj's underlying member method code with example usage: obj.forward(outputs, targets) and returns: loss