code: def transformer_nat_small():
      """Set of hyperparameters."""
      hparams = transformer.transformer_small()
      hparams.batch_size = 2048
      hparams.learning_rate = 0.2
      hparams.learning_rate_warmup_steps = 4000
      hparams.num_hidden_layers = 3
      hparams.hidden_size = 384
      hparams.filter_size = 2048
      hparams.label_smoothing = 0.0
      hparams.force_full_predict = True
      hparams.optimizer = "adam"
      hparams.optimizer_adam_epsilon = 1e-9
      hparams.optimizer_adam_beta1 = 0.9
      hparams.optimizer_adam_beta2 = 0.997
      hparams.add_hparam("bottleneck_kind", "vq")
      hparams.add_hparam("bottleneck_bits", 12)
      hparams.add_hparam("num_compress_steps", 3)
      hparams.add_hparam("beta", 0.25)
      hparams.add_hparam("epsilon", 1e-5)
      hparams.add_hparam("decay", 0.999)
      hparams.add_hparam("num_samples", 10)
      hparams.add_hparam("mask_startup_steps", 50000)
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  from six.moves import range
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import beam_search
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  from tensorflow.python.training import moving_averages
label_desc: Use image node_id 13 for calling a global function with example usage: transformer_nat_small() and returns: hparams