code: def test_kid(num_samples: int, arange_4d_image: Tensor, monkeypatch):
      if arange_4d_image.shape[1] != 3:
          return
  
      monkeypatch.setattr(
          neuralcompression.metrics._kid,
          "NoTrainInceptionV3",
          MockBackbone,
      )
      metric = KernelInceptionDistance()
  
      for _ in range(num_samples):
          sample1 = torch.randn_like(arange_4d_image).abs()
          sample1 = sample1 / sample1.max()
          sample1 = ncF.image_to_255_scale(sample1, torch.uint8)
          sample2 = torch.randn_like(arange_4d_image).abs()
          sample2 = sample2 / sample2.max()
          sample2 = ncF.image_to_255_scale(sample2, torch.uint8)
  
          metric.update(sample1, True)
          metric.update(sample2, False)
  
imports: import pytest
  import torch
  from conftest import MockBackbone
  from torch import Tensor
  import neuralcompression.functional
  import neuralcompression.metrics._kid
  from neuralcompression.metrics import KernelInceptionDistance
label_desc: Use image node_id 1 for calling a global function with example usage: test_kid(num_samples, arange_4d_image, monkeypatch) without return types