code: def universal_transformer_with_lstm_as_transition_function(
      layer_inputs,
      step,
      hparams,
      ffn_unit,
      attention_unit,
      pad_remover=None,
  ):
      """Universal Transformer which uses a lstm as transition function.
  
      It's kind of like having a lstm, 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: the original embedded inputs (= inputs to the first step)
          - memory: memory used in lstm.
        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: the original embedded inputs (= inputs to the first step)
            memory: contains information of state from all the previous steps.
      """
  
      state, unused_inputs, memory = tf.unstack(
          layer_inputs, num=None, axis=0, name="unstack"
      )
      # NOTE:
      # state (ut_state): output of the lstm in the previous step
      # inputs (ut_input): original input --> we don't use it here
      # memory: lstm memory
  
      # Multi_head_attention:
      assert (
          not hparams.add_step_timing_signal
      )  # Let lstm 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("lstm"):
          # lstm input gate: i_t = sigmoid(W_i.x_t + U_i.h_{t-1})
          transition_function_input_gate = _ffn_layer_multi_inputs(
              [transition_function_input, state],
              hparams,
              name="input",
              bias_initializer=tf.zeros_initializer(),
              activation=tf.sigmoid,
              pad_remover=pad_remover,
          )
  
          contrib.summary().scalar(
              "lstm_input_gate",
              tf.reduce_mean(transition_function_input_gate),
          )
  
          # lstm forget gate: f_t = sigmoid(W_f.x_t + U_f.h_{t-1})
          transition_function_forget_gate = _ffn_layer_multi_inputs(
              [transition_function_input, state],
              hparams,
              name="forget",
              bias_initializer=tf.zeros_initializer(),
              activation=None,
              pad_remover=pad_remover,
          )
          forget_bias_tensor = tf.constant(hparams.lstm_forget_bias)
          transition_function_forget_gate = tf.sigmoid(
              transition_function_forget_gate + forget_bias_tensor
          )
  
          contrib.summary().scalar(
              "lstm_forget_gate",
              tf.reduce_mean(transition_function_forget_gate),
          )
  
          # lstm output gate: o_t = sigmoid(W_o.x_t + U_o.h_{t-1})
          transition_function_output_gate = _ffn_layer_multi_inputs(
              [transition_function_input, state],
              hparams,
              name="output",
              bias_initializer=tf.zeros_initializer(),
              activation=tf.sigmoid,
              pad_remover=pad_remover,
          )
  
          contrib.summary().scalar(
              "lstm_output_gate",
              tf.reduce_mean(transition_function_output_gate),
          )
  
          # lstm input modulation
          transition_function_input_modulation = (
              _ffn_layer_multi_inputs(
                  [transition_function_input, state],
                  hparams,
                  name="input_modulation",
                  bias_initializer=tf.zeros_initializer(),
                  activation=tf.tanh,
                  pad_remover=pad_remover,
              )
          )
  
          transition_function_memory = (
              memory * transition_function_forget_gate
              + transition_function_input_gate
              * transition_function_input_modulation
          )
  
          transition_function_output = (
              tf.tanh(transition_function_memory)
              * transition_function_output_gate
          )
  
      transition_function_output = common_layers.layer_preprocess(
          transition_function_output, hparams
      )
  
      return (
          transition_function_output,
          unused_inputs,
          transition_function_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 14 for calling a global function with example usage: universal_transformer_with_lstm_as_transition_function(layer_inputs, step, hparams, ffn_unit, attention_unit, pad_remover) and returns: transition_function_output,  unused_inputs,  transition_function_memory