code: def local_attention1d_masked_decoder(
      x, kv_dim, heads_dim, feedforward_dim, hparams
  ):
      """Image Transformer decoder with local1D masked layers."""
      print(x)
      _, length_dim, model_dim = x.shape.dims
      mode = getattr(hparams, "mode", tf_estimator.ModeKeys.TRAIN)
      is_training = mode == tf_estimator.ModeKeys.TRAIN
      for layer in range(hparams.num_decoder_layers):
          layer_name = "decoder_layer_%d" % layer
          with tf.variable_scope(layer_name):
              # Self attention layer
              length_per_split = mtf.tensor_dim_to_size_per_split(
                  hparams.layout, hparams.mesh_shape, length_dim
              )
              x += layer_prepostprocess_dropout(
                  mtf.layers.masked_local_attention_1d(
                      mtf.layers.layer_norm(
                          x, model_dim, name="layer_norm_att"
                      ),
                      kv_dim,
                      heads_dim,
                      is_training,
                      window_size=hparams.block_length,
                      length_per_split=length_per_split,
                      name="self_att",
                  ),
                  hparams,
              )
              # ffn layer
              x += layer_prepostprocess_dropout(
                  mtf.layers.dense_relu_dense(
                      mtf.layers.layer_norm(
                          x, model_dim, name="layer_norm_ffn"
                      ),
                      feedforward_dim,
                      hparams.dropout,
                      dropout_broadcast_dims=[length_dim],
                  ),
                  hparams,
              )
  
      output = mtf.layers.layer_norm(
          x, model_dim, name="final_layer_norm"
      )
      return output
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import mesh_tensorflow
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import mtf_model
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 4 for calling a global function with example usage: local_attention1d_masked_decoder(x, kv_dim, heads_dim, feedforward_dim, hparams) and returns: output