code: def isemhash_unbottleneck(
      x, hidden_size, isemhash_filter_size_multiplier=1.0
  ):
      """Improved semantic hashing un-bottleneck."""
      filter_size = int(hidden_size * isemhash_filter_size_multiplier)
      x = 0.5 * (x - 1.0)  # Move from [-1, 1] to [0, 1].
      with tf.variable_scope("isemhash_unbottleneck"):
          h1a = tf.layers.dense(x, filter_size, name="hidden1a")
          h1b = tf.layers.dense(1.0 - x, filter_size, name="hidden1b")
          h2 = tf.layers.dense(
              tf.nn.relu(h1a + h1b), filter_size, name="hidden2"
          )
          return tf.layers.dense(
              tf.nn.relu(h2), hidden_size, name="final"
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from functools import partial
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_image_attention
  from tensor2tensor.layers import common_layers
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  import tensorflow_probability
  from tensorflow.python.training import moving_averages
label_desc: Use image node_id 26 for calling a global function with example usage: isemhash_unbottleneck(x, hidden_size, isemhash_filter_size_multiplier) and returns: tf