def METHOD_NAME(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