code: def add_evolved_transformer_hparams(hparams):
      """Add Evolved Transformer hparams.
  
      Note: These are for the Adam optimizer, not the Adafactor optimizer used in
      the paper.
  
      Args:
        hparams: Current hparams.
  
      Returns:
        hparams updated with Evolved Transformer values.
      """
      # Evolved Transformer "layers" are twice as deep as Transformer, so roughly
      # halve the number that we use. These numbers are taken from
      # arxiv.org/abs/1901.11117 .
      hparams.num_encoder_layers = 3
      hparams.num_decoder_layers = 4
  
      # Learning rate and decay scheme that mimics the transformer Adam config,
      # but with cosine decay instead of rsqrt.
      hparams.learning_rate_constant /= (
          hparams.learning_rate_warmup_steps**0.5
      )
      hparams.learning_rate_schedule = "constant*linear_warmup*single_cycle_cos_decay*rsqrt_hidden_size"
      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.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.python.ops import inplace_ops
label_desc: Use image node_id 6 for calling a global function with example usage: add_evolved_transformer_hparams(hparams) and returns: hparams