code: def forward(
      self,
      feature_ref,
      feature_mix,
  ):
      # feature_ref: B, T, F
      # feature_mix: B, T, F
      x = feature_ref.permute(0, 2, 1).unsqueeze(1)
      y = feature_mix.permute(0, 2, 1).unsqueeze(1)
  
      t = (
          torch.rand(x.shape[0], device=x.device)
          * (self.sde.T - self.t_eps)
          + self.t_eps
      )
      mean, std = self.sde.marginal_prob(x, t, y)
      z = torch.randn_like(x)  # i.i.d. normal distributed with var=0.5
      sigmas = std[:, None, None, None]
      perturbed_data = mean + sigmas * z
  
      score = self.score_fn(perturbed_data, t, y)
      assert (
          score.shape == x.shape
      ), "Check the output shape of the score_fn."
      err = score * sigmas + z
      loss = self._loss(err)
  
      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 6 for calling the ScoreModel obj's underlying member method code with example usage: obj.forward(feature_ref, feature_mix) and returns: loss