code: def distill_resnet_32_to_15_cifar20x5():
      """Set of hyperparameters."""
      hparams = distill_base()
      hparams.teacher_model = "resnet"
      hparams.teacher_hparams = "resnet_cifar_32"
      hparams.student_model = "resnet"
      hparams.student_hparams = "resnet_cifar_15"
  
      hparams.optimizer_momentum_nesterov = True
      # (base_lr=0.1) * (batch_size=128*8 (on TPU, or 8 GPUs)=1024) / (256.)
      hparams.teacher_learning_rate = 0.25 * 128.0 * 8.0 / 256.0
      hparams.student_learning_rate = 0.2 * 128.0 * 8.0 / 256.0
      hparams.learning_rate_decay_scheme = "piecewise"
      hparams.add_hparam(
          "learning_rate_boundaries", [40000, 60000, 80000]
      )
      hparams.add_hparam("learning_rate_multiples", [0.1, 0.01, 0.001])
  
      hparams.task_balance = 0.28
      hparams.distill_temperature = 2.0
  
      hparams.num_classes = 20
  
      return hparams
  
file_path: tensor2tensor/tensor2tensor/models/distillation.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_hparams
  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 2 for calling a global function with example usage: distill_resnet_32_to_15_cifar20x5() and returns: hparams