code: def alexnet():
      model = ModelHelper(
          name="r", arg_scope={"order": "NCHW", "is_test": True}
      )
      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, pad=0, legacy_pad=3
      )
      lrn1 = brew.lrn(
          model,
          pool1,
          "pool1_lrn",
          size=5,
          alpha=1.0e-4,
          beta=0.75,
          bias=1.0,
      )
      conv2 = brew.conv(
          model,
          lrn1,
          "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)
      lrn2 = brew.lrn(
          model,
          pool2,
          "pool2_lrn",
          size=5,
          alpha=1.0e-4,
          beta=0.75,
          bias=1.0,
      )
      conv3 = brew.conv(
          model,
          lrn2,
          "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")
      drop7 = brew.dropout(
          model, relu7, "fc7_dropout", is_test=1, ratio=0.5
      )
      fc8 = brew.fc(
          model,
          drop7,
          "fc8",
          4096,
          1000,
          ("XavierFill", {}),
          ("ConstantFill", {}),
      )
      relu8 = brew.relu(model, fc8, "fc8")
      brew.dropout(model, relu8, "fc8_dropout", is_test=1, ratio=0.5)
      return model, [(1, 3, 224, 224)]
  
imports: import unittest
  import numpy
  import copy
  from hypothesis import given
  import hypothesis.strategies
  from caffe2.python.model_helper import ModelHelper
  from caffe2.python.models import resnet
  from caffe2.python import workspace, brew
  import caffe2.python.hypothesis_test_util
  import caffe2.python.mkl.rewrite_graph
label_desc: Use image node_id 7 for calling a global function with example usage: alexnet() and returns: model,  list