code: def loss_hook(self, module, input, output):
      residue, forecast, target, bc_mask, fc_mask = output
      bc_loss = self(residue, pt.zeros_like(residue))
      fc_loss = self(
          forecast, target.unsqueeze(dim=2).expand_as(forecast)
      )
      if not (module.training and module.layerwise_loss):
          bc_loss = bc_loss[..., -1:, :]
          fc_loss = fc_loss[..., -1:, :]
      bc_loss = self._aggregate_loss(bc_loss, bc_mask)
      fc_loss = self._aggregate_loss(fc_loss, fc_mask)
  
      module.bc_loss = bc_loss.detach()
      module.fc_loss = fc_loss.detach()
      module.denominator = self._aggregate_loss(
          target.unsqueeze(dim=2), fc_mask
      )
  
      loss = bc_loss + module.tradeoff * fc_loss
      return loss
  
imports: from typing import TYPE_CHECKING, Optional
  import torch
  from torch import Tensor, BoolTensor
  from torch.nn import functional
label_desc: Use image node_id 3 for calling the LossFunction obj's underlying member method code with example usage: obj.loss_hook(module, input, output) and returns: loss