code: def resnet_cifar10(input, class_dim, depth=32):
      # depth should be one of 20, 32, 44, 56, 110, 1202
      assert (depth - 2) % 6 == 0
      n = (depth - 2) / 6
      nStages = {16, 64, 128}
      conv1 = conv_bn_layer(
          input, ch_in=3, ch_out=16, filter_size=3, stride=1, padding=1
      )
      res1 = layer_warp(basicblock, conv1, 16, n, 1)
      res2 = layer_warp(basicblock, res1, 32, n, 2)
      res3 = layer_warp(basicblock, res2, 64, n, 2)
      pool = paddle.layer.img_pool(
          input=res3,
          pool_size=8,
          stride=1,
          pool_type=paddle.pooling.Avg(),
      )
      out = paddle.layer.fc(
          input=pool, size=class_dim, act=paddle.activation.Softmax()
      )
      return out
  
file_path: MMdnn/mmdnn/conversion/examples/paddle/models/resnet.py
imports: import paddle.v2
label_desc: Use image node_id 7 for calling a global function with example usage: resnet_cifar10(input, class_dim, depth) and returns: out