code: def _cifar_config(is_training=True):
      drop_path_keep_prob = 1.0 if not is_training else 0.6
      return tf.contrib.training.HParams(
          stem_multiplier=3.0,
          drop_path_keep_prob=drop_path_keep_prob,
          num_cells=18,
          use_aux_head=1,
          num_conv_filters=32,
          dense_dropout_keep_prob=1.0,
          filter_scaling_rate=2.0,
          num_reduction_layers=2,
          data_format="NHWC",
          skip_reduction_layer_input=0,
          # 600 epochs with a batch size of 32
          # This is used for the drop path probabilities since it needs to increase
          # the drop out probability over the course of training.
          total_training_steps=937500,
      )
  
file_path: MMdnn/mmdnn/conversion/examples/tensorflow/models/nasnet.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from .None import nasnet_utils
label_desc: Use image node_id 1 for calling a global function with example usage: _cifar_config(is_training) and returns: tf