code: def cv_squared(x):
      """The squared coefficient of variation of a sample.
  
      Useful as a loss to encourage a positive distribution to be more uniform.
      Epsilons added for numerical stability.
      Returns 0 for an empty Tensor.
  
      Args:
        x: a `Tensor`.
  
      Returns:
        a `Scalar`.
      """
      epsilon = 1e-10
      float_size = tf.to_float(tf.size(x)) + epsilon
      mean = tf.reduce_sum(x) / float_size
      variance = (
          tf.reduce_sum(tf.squared_difference(x, mean)) / float_size
      )
      return variance / (tf.square(mean) + epsilon)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import math
  import six
  from six.moves import range
  from six.moves import zip
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers.vq_discrete import DiscreteBottleneck
  import tensorflow.compat.v1
label_desc: Use image node_id 8 for calling a global function with example usage: cv_squared(x) and returns: unknown