code: def get_logits(self, image):
      gauss_init = tf.random_normal_initializer(stddev=0.01)
      with argscope(
          Conv2D,
          kernel_initializer=tf.variance_scaling_initializer(scale=2.0),
      ), argscope(
          [Conv2D, FullyConnected], activation=tf.nn.relu
      ), argscope(
          [Conv2D, MaxPooling], data_format="channels_last"
      ):
          # necessary padding to get 55x55 after conv1
          image = tf.pad(image, [[0, 0], [2, 2], [2, 2], [0, 0]])
          l = Conv2D(
              "conv1",
              image,
              filters=96,
              kernel_size=11,
              strides=4,
              padding="VALID",
          )
          # size: 55
          visualize_conv1_weights(l.variables.W)
          l = tf.nn.lrn(
              l, 2, bias=1.0, alpha=2e-5, beta=0.75, name="norm1"
          )
          l = MaxPooling("pool1", l, 3, strides=2, padding="VALID")
          # 27
          l = Conv2D("conv2", l, filters=256, kernel_size=5, split=2)
          l = tf.nn.lrn(
              l, 2, bias=1.0, alpha=2e-5, beta=0.75, name="norm2"
          )
          l = MaxPooling("pool2", l, 3, strides=2, padding="VALID")
          # 13
          l = Conv2D("conv3", l, filters=384, kernel_size=3)
          l = Conv2D("conv4", l, filters=384, kernel_size=3, split=2)
          l = Conv2D("conv5", l, filters=256, kernel_size=3, split=2)
          l = MaxPooling("pool3", l, 3, strides=2, padding="VALID")
  
          l = FullyConnected(
              "fc6",
              l,
              4096,
              kernel_initializer=gauss_init,
              bias_initializer=tf.ones_initializer(),
          )
          l = Dropout(l, rate=0.5)
          l = FullyConnected(
              "fc7", l, 4096, kernel_initializer=gauss_init
          )
          l = Dropout(l, rate=0.5)
      logits = FullyConnected(
          "fc8", l, 1000, kernel_initializer=gauss_init
      )
      return logits
  
file_path: tensorpack/examples/ImageNetModels/alexnet.py
imports: import argparse
  import numpy
  import os
  import cv2
  from tensorpack import tfv1
  from tensorpack import *
  from tensorpack.dataflow import imgaug
  from tensorpack.tfutils import argscope
  from tensorpack.utils.gpu import get_num_gpu
  from imagenet_utils import ImageNetModel, get_imagenet_dataflow
label_desc: Use image node_id 1 for calling the Model obj's underlying member method code with example usage: obj.get_logits(image) and returns: logits