code: def _unpack_from_int_8_28(value, shape):
      """Inverse operation of `_pack_into_int_8_28`."""
      value = tf.reshape(value, [-1])
      extra_zeros = tf.zeros(
          tf.math.mod(-tf.shape(value), 2), value.dtype
      )
      val = tf.reshape(tf.concat([value, extra_zeros], 0), [-1, 2])
  
      a = tf.math.mod(val[:, 0], 2**8)
      b = tf.math.mod(tf.math.floordiv(val[:, 0], 2**8), 2**8)
      c = tf.math.mod(tf.math.floordiv(val[:, 0], 2**16), 2**8)
      d = (
          tf.math.floordiv(val[:, 0], 2**24)
          + tf.math.mod(val[:, 1], 2**4) * 2**4
      )
      e = tf.math.mod(tf.math.floordiv(val[:, 1], 2**4), 2**8)
      f = tf.math.mod(tf.math.floordiv(val[:, 1], 2**12), 2**8)
      g = tf.math.floordiv(val[:, 1], 2**20)
  
      unpacked_val = tf.reshape(
          tf.stack([a, b, c, d, e, f, g], 1),
          [
              -1,
          ],
      )
      unpacked_val = tf.slice(
          unpacked_val, [0], [tf.reduce_prod(shape)]
      )
      return tf.reshape(unpacked_val, shape)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import logging
  import math
  import numpy
  import tensorflow
label_desc: Use image node_id 17 for calling a global function with example usage: _unpack_from_int_8_28(value, shape) and returns: tf