code: def img2img_transformer2d_base():
      """Base params for img2img 2d attention."""
      hparams = image_transformer2d_base()
      # learning related flags
      hparams.layer_preprocess_sequence = "n"
      hparams.layer_postprocess_sequence = "da"
      # This version seems to benefit from a higher learning rate.
      hparams.learning_rate = 0.2
      hparams.layer_prepostprocess_dropout = 0.1
      hparams.learning_rate_warmup_steps = 12000
      hparams.filter_size = 2048
      hparams.num_encoder_layers = 4
      hparams.num_decoder_layers = 8
      hparams.bottom[
          "inputs"
      ] = modalities.image_channel_embeddings_bottom
      hparams.dec_attention_type = cia.AttentionType.LOCAL_2D
      hparams.block_raster_scan = True
      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 14 for calling a global function with example usage: img2img_transformer2d_base() and returns: hparams