code: def transformer_revnet_big():
      """Base hparams for TransformerRevnet."""
      hparams = transformer_revnet_base()
  
      # The TransformerRevnet uses significantly less memory than the Transformer.
      # Increase batch size and model size.
      hparams.batch_size *= 2
      hparams.hidden_size *= 2
      hparams.num_heads *= 2
      hparams.num_hidden_layers += 1
      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 4 for calling a global function with example usage: transformer_revnet_big() and returns: hparams