code: def test_score_based_diffusion_forward_backward_ncsnpp():
      parameters = {
          "score_model": "ncsnpp",
          "score_model_conf": {
              "nf": 4,
              "ch_mult": (1, 1, 1),
          },
          "sde": "ouve",
          "sde_conf": {},
      }
      model = ScoreModel(**parameters)
      model.train()
  
      real = torch.rand(2, 64, 256)
      imag = torch.rand(2, 64, 256)
      feature_mix = real + 1j * imag
      real = torch.rand(2, 64, 256)
      imag = torch.rand(2, 64, 256)
      feature_ref = real + 1j * imag
  
      loss = model(feature_ref, feature_mix)
      loss.abs().mean().backward()
  
imports: import pytest
  import torch
  from packaging.version import parse
  from torch import Tensor
  from espnet2.enh.diffusion.score_based_diffusion import ScoreModel
label_desc: Use image node_id 2 for calling a global function with example usage: test_score_based_diffusion_forward_backward_ncsnpp() without return types