code: def test_grad_through_normalize():
      tensor = torch.rand((2, 1, 28, 28))
      tensor.requires_grad_()
      mean = torch.tensor((0.0,))
      std = torch.tensor((1.0,))
      normalize = NormalizeByChannelMeanStd(mean, std)
  
      loss = (normalize(tensor) ** 2).sum()
      loss.backward()
  
      assert torch_allclose(2 * tensor, tensor.grad)
  
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 3 for calling a global function with example usage: test_grad_through_normalize() without return types