code: def img2img_transformer_dilated():
      """Try dilated."""
      hparams = img2img_transformer_base()
      hparams.add_hparam("num_memory_blocks", 1)
      hparams.num_heads = 8
      hparams.attention_key_channels = (
          hparams.attention_value_channels
      ) = 0
      hparams.hidden_size = 512
      hparams.filter_size = 2048
      hparams.num_decoder_layers = 8
      hparams.sampling_method = "random"
      hparams.gap_sizes = [0, 16, 64, 0, 16, 64, 128, 0]
      hparams.dec_attention_type = cia.AttentionType.DILATED
      hparams.img_len = 64
      hparams.block_length = 128
      hparams.block_width = 128
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import numpy
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_image_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 32 for calling a global function with example usage: img2img_transformer_dilated() and returns: hparams