code: def universal_transformer_base():
      """Base parameters for Universal Transformer."""
      hparams = transformer.transformer_base()
      # To have a similar capacity to the transformer_base with 6 layers,
      # we need to increase the size of the UT's layer
      # since, in fact, UT has a single layer repeating multiple times.
      hparams.hidden_size = 1024
      hparams.filter_size = 4096
      hparams.num_heads = 16
      hparams.layer_prepostprocess_dropout = 0.3
      hparams = update_hparams_for_universal_transformer(hparams)
      return hparams
  
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.models.research import universal_transformer_util
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling a global function with example usage: universal_transformer_base() and returns: hparams