code: def transformer_decoder_layers(
      name, n_layers, decoder_input, **kwargs
  ):
      """A transformation block composed of transformer decoder layers."""
      with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
          hparams = kwargs["hparams"]
          outputs = decoder_input
          with tf.variable_scope("decoder", reuse=tf.AUTO_REUSE):
              for layer_idx in range(n_layers):
                  outputs = transformer_decoder_layer(
                      decoder_input=outputs,
                      layer_idx=layer_idx,
                      **kwargs,
                  )
              outputs = common_layers.layer_preprocess(outputs, hparams)
          return outputs
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import transformer_glow_layers_ops
  from tensor2tensor.models.transformer import transformer_decoder_layer
  from tensor2tensor.models.transformer import transformer_encoder
  from tensor2tensor.models.transformer import transformer_prepare_encoder
  from tensor2tensor.utils import learning_rate
  from tensor2tensor.utils import mlperf_log
  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: transformer_decoder_layers(name, n_layers, decoder_input) and returns: outputs