code: def test_per_image_standardization():
      imgs = np.random.normal(
          scale=1.0 / (3072**0.5), size=(10, 3, 32, 32)
      ).astype(np.float32)
      per_image_standardize = PerImageStandardize()
      pt_scaled = per_image_standardize(torch.tensor(imgs)).numpy()
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          tf_scaled = _run_tf_per_image_standardization(imgs)
      assert np.abs(pt_scaled - tf_scaled).max() < 0.001
  
imports: import warnings
  import numpy
  import torch
  import torchvision.transforms.functional
  from advertorch.utils import torch_allclose
  from advertorch.utils import clamp
  from advertorch.utils import CIFAR10_MEAN
  from advertorch.utils import CIFAR10_STD
  from advertorch.utils import MNIST_MEAN
  from advertorch.utils import MNIST_STD
  from advertorch.utils import NormalizeByChannelMeanStd
  from advertorch.utils import PerImageStandardize
  from advertorch.utils import torch_flip
  from advertorch_examples.utils import bchw2bhwc
  from advertorch_examples.utils import bhwc2bchw
label_desc: Use image node_id 5 for calling a global function with example usage: test_per_image_standardization() without return types