code: def inference_network():
      # The image is 32 * 32 with RGB representation.
      data_shape = [3, 32, 32]
      images = fluid.layers.data(
          name="pixel", shape=data_shape, dtype="float32"
      )
  
      # 可选的resnet深度20, 32, 44, 56, 110, 1202
      """
      实验数据
      深度为32
      Test with Pass 9, Loss 0.86, Acc 0.76
      深度为110
      Test with Pass 9, Loss 0.76, Acc 0.76
      """
      predict = resnet_cifar10(images, 32)
  
      return predict
  
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 1 for calling a global function with example usage: inference_network() and returns: predict