code: def distill_base():
      """Set of hyperparameters."""
      # Base
      hparams = common_hparams.basic_params1()
  
      # teacher/student parameters
      hparams.add_hparam("teacher_model", "")
      hparams.add_hparam("teacher_hparams", "")
      hparams.add_hparam("student_model", "")
      hparams.add_hparam("student_hparams", "")
  
      # Distillation parameters
      # WARNING: distill_phase hparam will be overwritten in /bin/t2t_distill.py
      hparams.add_hparam("distill_phase", None)
      hparams.add_hparam("task_balance", 1.0)
      hparams.add_hparam("distill_temperature", 1.0)
      hparams.add_hparam("num_classes", 10)
  
      # Optional Phase-specific hyperparameters
      hparams.add_hparam("teacher_learning_rate", None)
      hparams.add_hparam("student_learning_rate", None)
  
      # Training parameters (stolen from ResNet)
      hparams.batch_size = 128
      hparams.optimizer = "Momentum"
      hparams.optimizer_momentum_momentum = 0.9
      hparams.optimizer_momentum_nesterov = True
      hparams.weight_decay = 1e-4
      hparams.clip_grad_norm = 0.0
      # (base_lr=0.1) * (batch_size=128*8 (on TPU, or 8 GPUs)=1024) / (256.)
      hparams.learning_rate = 0.4
      hparams.learning_rate_decay_scheme = "cosine"
      # For image_imagenet224, 120k training steps, which effectively makes this a
      # cosine decay (i.e. no cycles).
      hparams.learning_rate_cosine_cycle_steps = 120000
      hparams.initializer = "normal_unit_scaling"
      hparams.initializer_gain = 2.0
  
      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 1 for calling a global function with example usage: distill_base() and returns: hparams