code: def _subnetwork_generator(checkpoint_dir):
      hparams = tf.contrib.training.HParams(
          clip_gradients=5.0,
          stem_multiplier=3.0,
          drop_path_keep_prob=0.6,
          num_cells=3,
          use_aux_head=False,
          aux_head_weight=0.4,
          label_smoothing=0.1,
          num_conv_filters=4,
          dense_dropout_keep_prob=1.0,
          filter_scaling_rate=2.0,
          complexity_decay_rate=0.9,
          num_reduction_layers=2,
          data_format="NHWC",
          skip_reduction_layer_input=0,
          initial_learning_rate=0.01,
          use_bounded_activation=False,
          weight_decay=0.0001,
          knowledge_distillation=improve_nas.KnowledgeDistillation.NONE,
          snapshot=False,
          learn_mixture_weights=False,
          mixture_weight_type=adanet.MixtureWeightType.SCALAR,
          model_version="cifar",
          total_training_steps=100,
      )
      return improve_nas.Generator(
          [
              tf.feature_column.numeric_column(
                  key="x", shape=[32, 32, 3]
              )
          ],
          seed=11,
          optimizer_fn=_optimizer,
          iteration_steps=3,
          checkpoint_dir=checkpoint_dir,
          hparams=hparams,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import shutil
  from absl import flags
  from absl.testing import parameterized
  import adanet
  from adanet.research.improve_nas.trainer import improve_nas
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 3 for calling a global function with example usage: _subnetwork_generator(checkpoint_dir) and returns: improve_nas