code: def _run_tf_per_image_standardization(imgs):
      import tensorflow.compat.v1 as tf
  
      tf.disable_v2_behavior()
      import tensorflow.image  # noqa: F401
  
      imgs = bchw2bhwc(imgs)
      placeholder = tf.placeholder(tf.float32, shape=imgs.shape)
      var_scaled = tf.map_fn(
          lambda img: tf.image.per_image_standardization(img),
          placeholder,
      )
  
      with tf.Session() as sess:
          tf_scaled = sess.run(
              var_scaled, feed_dict={placeholder: imgs}
          )
      return bhwc2bchw(tf_scaled)
  
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 4 for calling a global function with example usage: _run_tf_per_image_standardization(imgs) and returns: bhwc2bchw