code: def shakeshake_small():
      """Parameters for CIFAR-10. Gets to about 96% accuracy@700K steps, 1 GPU."""
      hparams = common_hparams.basic_params1()
      hparams.batch_size = 128
      hparams.hidden_size = 32
      hparams.layer_prepostprocess_dropout = 0.0
      hparams.dropout = 0
      hparams.label_smoothing = 0.0
      hparams.clip_grad_norm = (
          0.0  # No clipping for now, one can also try 2.0.
      )
      hparams.num_hidden_layers = 26
      hparams.learning_rate_decay_scheme = "cosine"
      # Model should be run for 700000 steps with batch size 128 (~1800 epochs)
      hparams.learning_rate_cosine_cycle_steps = 700000
      hparams.learning_rate = 0.2
      hparams.learning_rate_warmup_steps = (
          100  # That's basically unused.
      )
      hparams.initializer = "uniform_unit_scaling"
      hparams.initializer_gain = 1.0
      hparams.weight_decay = 1e-4
      hparams.optimizer = "Momentum"
      hparams.optimizer_momentum_momentum = 0.9
      hparams.add_hparam("shake_shake_num_branches", 2)
      hparams.add_hparam("shake_shake_concat", int(False))
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import hparam
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 5 for calling a global function with example usage: shakeshake_small() and returns: hparams