code: def test_msssim(num_samples: int, arange_4d_image: Tensor):
      if (
          arange_4d_image.shape[2] < 128
          or arange_4d_image.shape[3] < 128
      ):
          return
  
      metric = MultiscaleStructuralSimilarity()
  
      for _ in range(num_samples):
          sample1 = torch.randn_like(arange_4d_image).abs()
          sample1 = sample1 / sample1.max()
          sample2 = torch.randn_like(arange_4d_image).abs()
          sample2 = sample2 / sample2.max()
  
          metric.update(sample1, sample2)
  
      metric.compute()
  
imports: import pytest
  import torch
  from torch import Tensor
  from neuralcompression.metrics import MultiscaleStructuralSimilarity
label_desc: Use image node_id 1 for calling a global function with example usage: test_msssim(num_samples, arange_4d_image) without return types