code: def test_mnist_normalize():
      # MNIST
      tensor = torch.rand((16, 1, 28, 28))
      normalize = NormalizeByChannelMeanStd(MNIST_MEAN, MNIST_STD)
  
      assert torch_allclose(
          torch.stack(
              [
                  F.normalize(t, MNIST_MEAN, MNIST_STD)
                  for t in tensor.clone()
              ]
          ),
          normalize(tensor),
      )
  
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 1 for calling a global function with example usage: test_mnist_normalize() without return types