code: def _MiniAlexNetNoDropout(self, order):
      # First, AlexNet using the cnn wrapper.
      model = model_helper.ModelHelper(name="alexnet")
      conv1 = brew.conv(
          model,
          "data",
          "conv1",
          3,
          16,
          11,
          ("XavierFill", {}),
          ("ConstantFill", {}),
          stride=4,
          pad=0,
      )
      relu1 = brew.relu(model, conv1, "relu1")
      norm1 = brew.lrn(
          model, relu1, "norm1", size=5, alpha=0.0001, beta=0.75
      )
      pool1 = brew.max_pool(model, norm1, "pool1", kernel=3, stride=2)
      conv2 = brew.group_conv(
          model,
          pool1,
          "conv2",
          16,
          32,
          5,
          ("XavierFill", {}),
          ("ConstantFill", {"value": 0.1}),
          group=2,
          stride=1,
          pad=2,
      )
      relu2 = brew.relu(model, conv2, "relu2")
      norm2 = brew.lrn(
          model, relu2, "norm2", size=5, alpha=0.0001, beta=0.75
      )
      pool2 = brew.max_pool(model, norm2, "pool2", kernel=3, stride=2)
      conv3 = brew.conv(
          model,
          pool2,
          "conv3",
          32,
          64,
          3,
          ("XavierFill", {"std": 0.01}),
          ("ConstantFill", {}),
          pad=1,
      )
      relu3 = brew.relu(model, conv3, "relu3")
      conv4 = brew.group_conv(
          model,
          relu3,
          "conv4",
          64,
          64,
          3,
          ("XavierFill", {}),
          ("ConstantFill", {"value": 0.1}),
          group=2,
          pad=1,
      )
      relu4 = brew.relu(model, conv4, "relu4")
      conv5 = brew.group_conv(
          model,
          relu4,
          "conv5",
          64,
          32,
          3,
          ("XavierFill", {}),
          ("ConstantFill", {"value": 0.1}),
          group=2,
          pad=1,
      )
      relu5 = brew.relu(model, conv5, "relu5")
      pool5 = brew.max_pool(model, relu5, "pool5", kernel=3, stride=2)
      fc6 = brew.fc(
          model,
          pool5,
          "fc6",
          1152,
          1024,
          ("XavierFill", {}),
          ("ConstantFill", {"value": 0.1}),
      )
      relu6 = brew.relu(model, fc6, "relu6")
      fc7 = brew.fc(
          model,
          relu6,
          "fc7",
          1024,
          1024,
          ("XavierFill", {}),
          ("ConstantFill", {"value": 0.1}),
      )
      relu7 = brew.relu(model, fc7, "relu7")
      fc8 = brew.fc(
          model,
          relu7,
          "fc8",
          1024,
          5,
          ("XavierFill", {}),
          ("ConstantFill", {"value": 0.0}),
      )
      pred = brew.softmax(model, fc8, "pred")
      xent = model.LabelCrossEntropy([pred, "label"], "xent")
      loss = model.AveragedLoss([xent], ["loss"])
      model.AddGradientOperators([loss])
      return model
  
file_path: pytorch/caffe2/python/model_device_test.py
imports: import numpy
  import unittest
  from caffe2.proto import caffe2_pb2
  from caffe2.python import workspace, device_checker, test_util, model_helper, brew
label_desc: Use image node_id 1 for calling the TestMiniAlexNet obj's underlying member method code with example usage: obj._MiniAlexNetNoDropout(order) and returns: model