code: def train_network():
      predict = inference_network()
      label = fluid.layers.data(name="label", shape=[1], dtype="int64")
      cost = fluid.layers.cross_entropy(input=predict, label=label)
      avg_cost = fluid.layers.mean(cost)
      accuracy = fluid.layers.accuracy(input=predict, label=label)
      return [avg_cost, accuracy]
  
file_path: AdvBox/tutorials/cifar10_model.py
imports: from __future__ import print_function
  from __future__ import absolute_import
  import paddle
  import paddle.fluid
  import numpy
  import sys
  import os
  from .image_classification.resnet import resnet_cifar10
label_desc: Use image node_id 2 for calling a global function with example usage: train_network() and returns: list