code: def transformer_revnet_base():
      """Base hparams for TransformerRevnet."""
      hparams = transformer.transformer_big()
  
      # Use settings from transformer_n_da
      hparams.layer_preprocess_sequence = "n"
      hparams.layer_postprocess_sequence = "da"
      hparams.learning_rate = 0.4
  
      return hparams
  
file_path: tensor2tensor/tensor2tensor/models/research/transformer_revnet.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import registry
  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_revnet_base() and returns: hparams