code: def _cifar_stem(inputs, hparams):
      """Stem used for models trained on Cifar."""
      num_stem_filters = int(
          hparams.num_conv_filters * hparams.stem_multiplier
      )
      net = slim.conv2d(
          inputs, num_stem_filters, 3, scope="l1_stem_3x3"
      )
      net = slim.batch_norm(net, scope="l1_stem_bn")
      return net, [None, net]
  
file_path: adanet/research/improve_nas/trainer/nasnet.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import tensorflow.compat.v1
label_desc: Use image node_id 10 for calling a global function with example usage: _cifar_stem(inputs, hparams) and returns: net,  list