code: def universal_transformer_layer(
      x, hparams, ffn_unit, attention_unit, pad_remover=None
  ):
      """Core function applying the universal transformer layer.
  
      Args:
        x: input
        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:
        the output tensor,  extra output (can be memory, ponder time, etc.)
  
      Raises:
        ValueError: Unknown recurrence type
      """
  
      def add_vanilla_transformer_layer(x, num_layers, name):
          """Passes the input through num_layers of vanilla transformer layers.
  
          Args:
           x: input
           num_layers: number of layers
           name: string, prefix of layer names
  
          Returns:
             output of vanilla_transformer_layer
          """
          if hparams.add_position_timing_signal:
              # In case of add_position_timing_signal=true, we set  hparams.pos=None
              # and add position timing signal at the beginning of each step, so for
              # the vanilla transformer, we need to add timing signal here.
              x = common_attention.add_timing_signal_1d(x)
          for layer in range(num_layers):
              with tf.variable_scope(name + "layer_%d" % layer):
                  x = ffn_unit(attention_unit(x))
          return x
  
      with tf.variable_scope(
          "universal_transformer_%s" % hparams.recurrence_type
      ):
          if (
              hparams.mix_with_transformer
              and "before_ut" in hparams.mix_with_transformer
          ):
              x = add_vanilla_transformer_layer(
                  x, hparams.num_mixedin_layers, "before_ut_"
              )
  
          if hparams.recurrence_type == "act":
              output, extra_output = universal_transformer_act(
                  x, hparams, ffn_unit, attention_unit
              )
  
          else:  # for all the other recurrency types with fixed number of steps
              ut_function, initializer = get_ut_layer(
                  x, hparams, ffn_unit, attention_unit, pad_remover
              )
  
              output, _, extra_output = tf.foldl(
                  ut_function,
                  tf.range(hparams.num_rec_steps),
                  initializer=initializer,
              )
  
              # Right now, this is only possible when the transition function is an lstm
              if hparams.recurrence_type == "lstm" and hparams.get(
                  "use_memory_as_final_state", False
              ):
                  output = extra_output
  
          if (
              hparams.mix_with_transformer
              and "after_ut" in hparams.mix_with_transformer
          ):
              output = add_vanilla_transformer_layer(
                  output, hparams.num_mixedin_layers, "after_ut_"
              )
  
          return output, extra_output
  
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 3 for calling a global function with example usage: universal_transformer_layer(x, hparams, ffn_unit, attention_unit, pad_remover) and returns: x, output,  extra_output