class_docstr: Distillation from a teacher to student network.
  
  First, a teacher is trained on a task; Second, a student is trained to perform
  the task while matching the teacher's softened outputs. For more details, see
  the paper below.
  
  In the hparams passed to this model include the desired
  {teacher/student}_model and {teacher/student}_hparams to be used. Also,
  specify the distillation temperature and task-distillation balance.
  
  Distilling the Knowledge in a Neural Network
  Hinton, Vinyals and Dean
  https://arxiv.org/abs/1503.02531
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code: def top(self, body_output, features):
      return body_output
  
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 3 for calling the Distillation obj's underlying member method code with example usage: obj.top(body_output, features) and returns: body_output