code: def universal_transformer_with_gru_as_transition_function(
      layer_inputs,
      step,
      hparams,
      ffn_unit,
      attention_unit,
      pad_remover=None,
  ):
      """Universal Transformer which uses a gru as transition function.
  
      It's kind of like having a gru, filliped vertically next to the Universal
      Transformer that controls the flow of the information in depth,
      over different steps of the Universal Transformer.
  
      Args:
        layer_inputs:
          - state: state
          - inputs: not used here
          - memory: not used here
        step: indicates number of steps taken so far
        hparams: model hyper-parameters.
        ffn_unit: feed-forward unit
        attention_unit: multi-head attention unit
        pad_remover: to mask out padding in convolutional layers (efficiency).
      Returns:
        layer_output:
            new_state: new state
            inputs: not uesed
            memory: not used
      """
  
      state, unused_inputs, unused_memory = tf.unstack(
          layer_inputs, num=None, axis=0, name="unstack"
      )
  
      # state (ut_state): output of the gru in the previous step
  
      # Multi_head_attention:
      assert not hparams.add_step_timing_signal  # Let gru count for us!
      mh_attention_input = step_preprocess(state, step, hparams)
      transition_function_input = attention_unit(mh_attention_input)
  
      # Transition Function:
      if hparams.add_ffn_unit_to_the_transition_function:
          transition_function_input = ffn_unit(
              transition_function_input
          )
  
      transition_function_input = common_layers.layer_preprocess(
          transition_function_input, hparams
      )
      with tf.variable_scope("gru"):
          # gru update gate: z_t = sigmoid(W_z.x_t + U_z.h_{t-1})
          transition_function_update_gate = _ffn_layer_multi_inputs(
              [transition_function_input, state],
              hparams,
              name="update",
              bias_initializer=tf.constant_initializer(1.0),
              activation=tf.sigmoid,
              pad_remover=pad_remover,
          )
  
          contrib.summary().scalar(
              "gru_update_gate",
              tf.reduce_mean(transition_function_update_gate),
          )
  
          # gru reset gate: r_t = sigmoid(W_r.x_t + U_r.h_{t-1})
          transition_function_reset_gate = _ffn_layer_multi_inputs(
              [transition_function_input, state],
              hparams,
              name="reset",
              bias_initializer=tf.constant_initializer(1.0),
              activation=tf.sigmoid,
              pad_remover=pad_remover,
          )
  
          contrib.summary().scalar(
              "gru_reset_gate",
              tf.reduce_mean(transition_function_reset_gate),
          )
          reset_state = transition_function_reset_gate * state
  
          # gru_candidate_activation: h' = tanh(W_{x_t} + U (r_t h_{t-1})
          transition_function_candidate = _ffn_layer_multi_inputs(
              [transition_function_input, reset_state],
              hparams,
              name="candidate",
              bias_initializer=tf.zeros_initializer(),
              activation=tf.tanh,
              pad_remover=pad_remover,
          )
  
          transition_function_output = (
              (1 - transition_function_update_gate)
              * transition_function_input
              + transition_function_update_gate
              * transition_function_candidate
          )
  
      transition_function_output = common_layers.layer_preprocess(
          transition_function_output, hparams
      )
  
      return transition_function_output, unused_inputs, unused_memory
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import functools
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import expert_utils
  import tensorflow.compat.v1
label_desc: Use image node_id 13 for calling a global function with example usage: universal_transformer_with_gru_as_transition_function(layer_inputs, step, hparams, ffn_unit, attention_unit, pad_remover) and returns: transition_function_output,  unused_inputs,  unused_memory