code: def test_dists(
      num_samples: int, arange_4d_image: Tensor, monkeypatch
  ):
      if arange_4d_image.shape[1] != 3:
          return
  
      rng = torch.Generator()
      rng.manual_seed(60)
  
      monkeypatch.setattr(
          neuralcompression.metrics._fid_swav,
          "NoTrainSwAV",
          MockBackbone,
      )
      metric = FrechetInceptionDistanceSwAV()
  
      for _ in range(num_samples):
          sample1 = torch.randn(
              size=arange_4d_image.shape, generator=rng
          ).abs()
          sample1 = sample1 / sample1.max()
          sample1 = ncF.image_to_255_scale(sample1, torch.uint8)
          sample2 = torch.randn(
              size=arange_4d_image.shape, generator=rng
          ).abs()
          sample2 = sample2 / sample2.max()
          sample2 = ncF.image_to_255_scale(sample2, torch.uint8)
  
          metric.update(sample1, True)
          metric.update(sample2, False)
  
      metric.compute()
  
imports: import pytest
  import torch
  from conftest import MockBackbone
  from torch import Tensor
  import neuralcompression.functional
  import neuralcompression.metrics._fid_swav
  from neuralcompression.metrics import FrechetInceptionDistanceSwAV
label_desc: Use image node_id 1 for calling a global function with example usage: test_dists(num_samples, arange_4d_image, monkeypatch) without return types