code: def reverse_part(inputs, hparams, n_bits):
      """Reverse part of Benes block.
  
      Repeatably applies interleaved Residual Switch layer and Reverse Shuffle
      Layer. One set of weights used for all Switch layers.
  
      Args:
        inputs: inputs for reverse part. Should be outputs from forward part.
        hparams: params of the network.
        n_bits: count of repeated layer applications.
  
      Returns:
        tf.Tensor: output of reverse part.
      """
      reverse_rsu = RSU("reverse_switch", hparams.dropout, hparams.mode)
  
      def reverse_step(state, _):
          with tf.variable_scope("reverse"):
              new_state = reverse_rsu(state)
              return reverse_shuffle_layer(new_state)
  
      reverse_outputs = tf.scan(
          reverse_step,
          tf.range(n_bits, n_bits * 2),
          initializer=inputs,
          parallel_iterations=1,
          swap_memory=True,
      )
  
      return reverse_outputs[-1, :, :, :]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from tensor2tensor.layers.common_layers import gelu
  from tensor2tensor.models.research.shuffle_network import reverse_shuffle_layer
  from tensor2tensor.models.research.shuffle_network import shuffle_layer
  from tensor2tensor.models.research.shuffle_network import ShuffleNetwork
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 3 for calling a global function with example usage: reverse_part(inputs, hparams, n_bits) and returns: reverse_outputs, reverse_shuffle_layer