code: def forward(self, energy_predicted, energy_tgt, length):
      if energy_tgt is None:
          return 0.0
      dur_mask = mask_from_lens(length, max_len=energy_tgt.size(1))
      energy_loss = F.mse_loss(
          energy_tgt, energy_predicted, reduction="none"
      )
      energy_loss = (energy_loss * dur_mask).sum() / dur_mask.sum()
      energy_loss *= self.loss_scale
  
      return energy_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 EnergyLoss obj's underlying member method code with example usage: obj.forward(energy_predicted, energy_tgt, length) and returns: energy_loss, int