code: def build_nasnet_cifar(
      images,
      num_classes,
      is_training=True,
      config=None,
      current_step=None,
  ):
      """Build NASNet model for the Cifar Dataset."""
      hparams = (
          cifar_config() if config is None else copy.deepcopy(config)
      )
      _update_hparams(hparams, is_training)
  
      if tf.test.is_gpu_available() and hparams.data_format == "NHWC":
          tf.logging.info(
              "A GPU is available on the machine, consider using NCHW "
              "data format for increased speed on GPU."
          )
  
      if hparams.data_format == "NCHW":
          images = tf.transpose(images, [0, 3, 1, 2])
  
      # Calculate the total number of cells in the network
      # Add 2 for the reduction cells
      total_num_cells = hparams.num_cells + 2
  
      normal_cell = nasnet_utils.NasNetANormalCell(
          hparams.num_conv_filters,
          hparams.drop_path_keep_prob,
          total_num_cells,
          hparams.total_training_steps,
          hparams.use_bounded_activation,
      )
      reduction_cell = nasnet_utils.NasNetAReductionCell(
          hparams.num_conv_filters,
          hparams.drop_path_keep_prob,
          total_num_cells,
          hparams.total_training_steps,
          hparams.use_bounded_activation,
      )
      with arg_scope(
          [slim.dropout, nasnet_utils.drop_path, slim.batch_norm],
          is_training=is_training,
      ):
          with arg_scope(
              [
                  slim.avg_pool2d,
                  slim.max_pool2d,
                  slim.conv2d,
                  slim.batch_norm,
                  slim.separable_conv2d,
                  nasnet_utils.factorized_reduction,
                  nasnet_utils.global_avg_pool,
                  nasnet_utils.get_channel_index,
                  nasnet_utils.get_channel_dim,
              ],
              data_format=hparams.data_format,
          ):
              return _build_nasnet_base(
                  images,
                  normal_cell=normal_cell,
                  reduction_cell=reduction_cell,
                  num_classes=num_classes,
                  hparams=hparams,
                  is_training=is_training,
                  stem_type="cifar",
                  current_step=current_step,
              )
  
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 11 for calling a global function with example usage: build_nasnet_cifar(images, num_classes, is_training, config, current_step) and returns: _build_nasnet_base