code: def center_advs(
      advs, axes, eps, offset=0, scale=1, name="center_adv"
  ):
      """Normalize the advs tensor.
  
      This calculates the mean and variance using the axes specified
      and normalizes the tensor using those values.
  
      Args:
          advs (tf.Tensor): Tensor to normalize.
          axes (array[int]): Axes along which to compute the mean and variance.
          eps (float): Small number to avoid dividing by zero.
          offset (tf.Tensor): Offset added to the normalized tensor.
              This is zero by default.
          scale (tf.Tensor): Scale to apply to the normalized tensor. This is
              1 by default but can also be None.
          name (string): Name of the operation. None by default.
  
      Returns:
          tf.Tensor: Normalized, scaled and offset tensor.
      """
      with tf.name_scope(name):
          mean, var = tf.nn.moments(advs, axes=axes)
          advs = tf.nn.batch_normalization(
              advs, mean, var, offset, scale, eps
          )
  
      return advs
  
imports: import collections
  import numpy
  import tensorflow
label_desc: Use image node_id 20 for calling a global function with example usage: center_advs(advs, axes, eps, offset, scale, name) and returns: advs