code: def cv_squared(x):
    
    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)
docs: 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`.