code: def get_hyper_parameters():
      """Returns the hyper-parameters used for training."""
      hparams = HParams()
      if FLAGS.train_epochs:
          hparams.train_epochs = FLAGS.train_epochs
      if FLAGS.eval_steps:
          hparams.eval_steps = FLAGS.eval_steps
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import threading
  from absl import app
  from absl import flags
  from absl import logging
  import attr
  import neural_structured_learning
  from research.carls import dynamic_embedding_config_pb2
  from research.carls import dynamic_embedding_neighbor_cache
  from research.carls import graph_regularization
  from research.carls import kbs_server_helper_pybind
  import tensorflow
  from google.protobuf import text_format
label_desc: Use image node_id 1 for calling a global function with example usage: get_hyper_parameters() and returns: hparams