code: def score_fn(self, x, t, y):
      # Concatenate y as an extra channel
      dnn_input = torch.cat([x, y], dim=1)
  
      # the minus is most likely unimportant here - taken from Song's repo
      score = -self.dnn(dnn_input, t)
      return score
  
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 5 for calling the ScoreModel obj's underlying member method code with example usage: obj.score_fn(x, t, y) and returns: score