code: def AlexNet(order, cudnn_ws):
      my_arg_scope = {
          "order": order,
          "use_cudnn": True,
          "cudnn_exhaustive_search": True,
      }
      if cudnn_ws:
          my_arg_scope["ws_nbytes_limit"] = cudnn_ws
      model = model_helper.ModelHelper(
          name="alexnet",
          arg_scope=my_arg_scope,
      )
      conv1 = brew.conv(
          model,
          "data",
          "conv1",
          3,
          64,
          11,
          ("XavierFill", {}),
          ("ConstantFill", {}),
          stride=4,
          pad=2,
      )
      relu1 = brew.relu(model, conv1, "conv1")
      pool1 = brew.max_pool(model, relu1, "pool1", kernel=3, stride=2)
      conv2 = brew.conv(
          model,
          pool1,
          "conv2",
          64,
          192,
          5,
          ("XavierFill", {}),
          ("ConstantFill", {}),
          pad=2,
      )
      relu2 = brew.relu(model, conv2, "conv2")
      pool2 = brew.max_pool(model, relu2, "pool2", kernel=3, stride=2)
      conv3 = brew.conv(
          model,
          pool2,
          "conv3",
          192,
          384,
          3,
          ("XavierFill", {}),
          ("ConstantFill", {}),
          pad=1,
      )
      relu3 = brew.relu(model, conv3, "conv3")
      conv4 = brew.conv(
          model,
          relu3,
          "conv4",
          384,
          256,
          3,
          ("XavierFill", {}),
          ("ConstantFill", {}),
          pad=1,
      )
      relu4 = brew.relu(model, conv4, "conv4")
      conv5 = brew.conv(
          model,
          relu4,
          "conv5",
          256,
          256,
          3,
          ("XavierFill", {}),
          ("ConstantFill", {}),
          pad=1,
      )
      relu5 = brew.relu(model, conv5, "conv5")
      pool5 = brew.max_pool(model, relu5, "pool5", kernel=3, stride=2)
      fc6 = brew.fc(
          model,
          pool5,
          "fc6",
          256 * 6 * 6,
          4096,
          ("XavierFill", {}),
          ("ConstantFill", {}),
      )
      relu6 = brew.relu(model, fc6, "fc6")
      fc7 = brew.fc(
          model,
          relu6,
          "fc7",
          4096,
          4096,
          ("XavierFill", {}),
          ("ConstantFill", {}),
      )
      relu7 = brew.relu(model, fc7, "fc7")
      fc8 = brew.fc(
          model,
          relu7,
          "fc8",
          4096,
          1000,
          ("XavierFill", {}),
          ("ConstantFill", {}),
      )
      pred = brew.softmax(model, fc8, "pred")
      xent = model.net.LabelCrossEntropy([pred, "label"], "xent")
      model.net.AveragedLoss(xent, "loss")
      return model, 224
  
file_path: pytorch/caffe2/python/convnet_benchmarks.py
imports: import argparse
  from caffe2.python import workspace, brew, model_helper
label_desc: Use image node_id 2 for calling a global function with example usage: AlexNet(order, cudnn_ws) and returns: model,  int