code: def create_network_lrn():
      init1 = Gaussian(scale=0.01)
      init2 = Gaussian(scale=0.005)
  
      layers = [
          Conv(
              (11, 11, 96),
              padding=0,
              strides=4,
              init=init1,
              bias=Constant(0),
              activation=Rectlin(),
              name="conv1",
          ),
          Pooling(3, strides=2, name="pool1"),
          LRN(5, ascale=0.0001, bpower=0.75, name="norm1"),
          Conv(
              (5, 5, 256),
              padding=2,
              init=init1,
              bias=Constant(1.0),
              activation=Rectlin(),
              name="conv2",
          ),
          Pooling(3, strides=2, name="pool2"),
          LRN(5, ascale=0.0001, bpower=0.75, name="norm2"),
          Conv(
              (3, 3, 384),
              padding=1,
              init=init1,
              bias=Constant(0),
              activation=Rectlin(),
              name="conv3",
          ),
          Conv(
              (3, 3, 384),
              padding=1,
              init=init1,
              bias=Constant(1.0),
              activation=Rectlin(),
              name="conv4",
          ),
          Conv(
              (3, 3, 256),
              padding=1,
              init=init1,
              bias=Constant(1.0),
              activation=Rectlin(),
              name="conv5",
          ),
          Pooling(3, strides=2, name="pool5"),
          Affine(
              nout=4096,
              init=init2,
              bias=Constant(1.0),
              activation=Rectlin(),
              name="fc6",
          ),
          Dropout(keep=0.5, name="drop6"),
          Affine(
              nout=4096,
              init=init2,
              bias=Constant(1.0),
              activation=Rectlin(),
              name="fc7",
          ),
          Dropout(keep=0.5, name="drop7"),
          Affine(
              nout=1000,
              init=init1,
              bias=Constant(0.0),
              activation=Softmax(),
              name="fc8",
          ),
      ]
  
      return Model(layers=layers), GeneralizedCost(
          costfunc=CrossEntropyMulti()
      )
  
imports: from neon.initializers import Gaussian, Constant
  from neon.layers import Conv, Pooling, GeneralizedCost, Affine, Dropout, LRN
  from neon.transforms import Rectlin, Softmax, CrossEntropyMulti
  from neon.models import Model
label_desc: Use image node_id 2 for calling a global function with example usage: create_network_lrn() and returns: Model,  GeneralizedCost