code: def actnorm_center(name, x, reverse=False, init=False):
      """Add a bias to x.
  
      Initialize such that the output of the first minibatch is zero centered
      per channel.
  
      Args:
        name: scope
        x: 2-D or 4-D Tensor.
        reverse: Forward or backward operation.
        init: data-dependent initialization.
  
      Returns:
        x_center: (x + b), if reverse is True and (x - b) otherwise.
      """
      shape = common_layers.shape_list(x)
      with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
          assert len(shape) == 2 or len(shape) == 4
          if len(shape) == 2:
              x_mean = tf.reduce_mean(x, [0], keepdims=True)
              b = get_variable_ddi(
                  "b", (1, shape[1]), initial_value=-x_mean, init=init
              )
          elif len(shape) == 4:
              x_mean = tf.reduce_mean(x, [0, 1, 2], keepdims=True)
              b = get_variable_ddi(
                  "b",
                  (1, 1, 1, shape[3]),
                  initial_value=-x_mean,
                  init=init,
              )
  
          if not reverse:
              x += b
          else:
              x -= b
          return x
  
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 14 for calling a global function with example usage: actnorm_center(name, x, reverse, init) and returns: x