class_docstr: enc(x) = sign(x) * (abs(x)*128)^8.
  
  This provides less range and more resolution.
  The range of representable positive values is approximately [2^-23, 2^9]
  Resolution is 8x better than bfloat16.
code: def decode(self, x):
      x = tf.to_float(x)
      # we can't use tf.pow(..., 0.125) because of a high-error approximation
      # on TPU.  Instead we sqrt three times.
      return tf.sign(x) * (tf.sqrt(tf.sqrt(tf.sqrt(tf.abs(x)))) / 128.0)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow.compat.v1
  from tensorflow.python.framework import function
label_desc: Use image node_id 2 for calling the EighthPowerEncoding obj's underlying member method code with example usage: obj.decode(x) and returns: unknown