code: def _loss(self, err):
      if self.loss_type == "mse":
          losses = torch.square(err.abs())
      elif self.loss_type == "mae":
          losses = err.abs()
      # taken from reduce_op function: sum over channels and position
      # and mean over batch dim presumably only important for absolute
      # loss number, not for gradients
      loss = torch.mean(
          0.5 * torch.sum(losses.reshape(losses.shape[0], -1), dim=-1)
      )
      return loss
  
file_path: espnet/espnet2/enh/diffusion/score_based_diffusion.py
imports: import math
  from abc import ABC, abstractmethod
  from collections import OrderedDict
  from typing import Dict, Optional, Tuple
  import torch
  import espnet2.enh.diffusion.sampling
  from espnet2.enh.diffusion.abs_diffusion import AbsDiffusion
  from espnet2.enh.diffusion.sdes import OUVESDE, OUVPSDE, SDE
  from espnet2.enh.layers.dcunet import DCUNet
  from espnet2.enh.layers.ncsnpp import NCSNpp
  from espnet2.train.class_choices import ClassChoices
label_desc: Use image node_id 2 for calling the ScoreModel obj's underlying member method code with example usage: obj._loss(err) and returns: loss