code: def test_score_based_diffusion_sampling(sampler_type):
      parameters = {
          "score_model": "ncsnpp",
          "score_model_conf": {
              "nf": 4,
              "ch_mult": (1, 1, 1),
          },
          "sde": "ouve",
          "sde_conf": {},
      }
      model = ScoreModel(**parameters)
      model.eval()
  
      real = torch.rand(2, 64, 256)
      imag = torch.rand(2, 64, 256)
      noise = real + 1j * imag
  
      output = model.enhance(noise, N=2, sampler_type=sampler_type)
  
      assert output.shape == noise.shape
  
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 3 for calling a global function with example usage: test_score_based_diffusion_sampling(sampler_type) without return types