code: def actnorm(
      name,
      x,
      logscale_factor=3.0,
      reverse=False,
      init=False,
      trainable=True,
  ):
      """x_{ij} = s x x_{ij} + b. Per-channel scaling and bias.
  
      If init is set to True, the scaling and bias are initialized such
      that the mean and variance of the output activations of the first minibatch
      are zero and one respectively.
  
      Args:
        name: variable scope.
        x: input
        logscale_factor: Used in actnorm_scale. Optimizes f(ls*s') instead of f(s)
                         where s' = s / ls. Helps in faster convergence.
        reverse: forward or reverse operation.
        init: Whether or not to do data-dependent initialization.
        trainable:
  
      Returns:
        x: output after adding bias and scaling.
        objective: log(sum(s))
      """
      var_arg_scope = arg_scope([get_variable_ddi], trainable=trainable)
      var_scope = tf.variable_scope(name, reuse=tf.AUTO_REUSE)
  
      with var_scope, var_arg_scope:
          if not reverse:
              x = actnorm_center(
                  name + "_center", x, reverse, init=init
              )
              x, objective = actnorm_scale(
                  name + "_scale",
                  x,
                  logscale_factor=logscale_factor,
                  reverse=reverse,
                  init=init,
              )
          else:
              x, objective = actnorm_scale(
                  name + "_scale",
                  x,
                  logscale_factor=logscale_factor,
                  reverse=reverse,
                  init=init,
              )
              x = actnorm_center(
                  name + "_center", x, reverse, init=init
              )
          return x, objective
  
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 13 for calling a global function with example usage: actnorm(name, x, logscale_factor, reverse, init, trainable) and returns: x,  objective