code: def actnorm_3d(name, x, logscale_factor=3.0):
      """Applies actnorm to each time-step independently.
  
      There are a total of 2*n_channels*n_steps parameters learnt.
  
      Args:
        name: variable scope.
        x: 5-D Tensor, (NTHWC)
        logscale_factor: Increases the learning rate of the scale by
                         logscale_factor.
      Returns:
        x: 5-D Tensor, (NTHWC) with the per-timestep, per-channel normalization.
      """
      with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
          x = tf.unstack(x, axis=1)
          x_normed = []
          for ind, x_step in enumerate(x):
              x_step, _ = actnorm(
                  "actnorm_%d" % ind,
                  x_step,
                  logscale_factor=logscale_factor,
              )
              x_normed.append(x_step)
          return tf.stack(x_normed, axis=1), None
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import numpy
  import scipy
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import common_video
  from tensor2tensor.utils import contrib
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  import tensorflow_probability
label_desc: Use image node_id 12 for calling a global function with example usage: actnorm_3d(name, x, logscale_factor) and returns: tf,  None