code: def transformer_between_predictions_layer(
      x,
      hparams,
      name,
      cache=None,
      decode_loop_step=None,
      nonpadding=None,
      losses=None,
      layer_collection=None,
  ):
      """Stack between prediction layers."""
      with tf.variable_scope(name):
          for i in range(hparams.ffn_in_prediction_cascade):
              with tf.variable_scope("layer_%d" % i):
                  y = transformer_layers.transformer_ffn_layer(
                      common_layers.layer_preprocess(
                          x, hparams, layer_collection=layer_collection
                      ),
                      hparams,
                      conv_padding="LEFT",
                      nonpadding_mask=nonpadding,
                      losses=losses,
                      cache=cache,
                      decode_loop_step=decode_loop_step,
                      layer_collection=layer_collection,
                  )
                  x = common_layers.layer_postprocess(x, y, hparams)
      return x
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import logging
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import transformer_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
label_desc: Use image node_id 8 for calling a global function with example usage: transformer_between_predictions_layer(x, hparams, name, cache, decode_loop_step, nonpadding, losses, layer_collection) and returns: x