code: def evolved_transformer_tpu_basic():
      """Basic Seq2Seq TPU hyper-parameters."""
      hparams = transformer.transformer_big_tpu()
      hparams.add_hparam("print_vars", False)
      hparams.batch_size = 8192
      hparams.max_length = 256
  
      # N < 0 means all weights in the model are trainable.
      # N >= 0 means all weights are frozen except N top decoder layers +
      # (pre-)softmax matrix (that projects from hidden size to vocab size).
      hparams.add_hparam("num_trainable_top_decoder_layers", -1)
  
      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 13 for calling a global function with example usage: evolved_transformer_tpu_basic() and returns: hparams