code: def get_logits(self, image):
      with argscope(
          Conv2D,
          kernel_initializer=tf.variance_scaling_initializer(scale=2.0),
      ), argscope(
          [Conv2D, MaxPooling, BatchNorm], data_format="channels_first"
      ):
          logits = (
              LinearWrap(image)
              .apply(convnormrelu, "conv1_1", 64)
              .apply(convnormrelu, "conv1_2", 64)
              .MaxPooling("pool1", 2)
              # 112
              .apply(convnormrelu, "conv2_1", 128)
              .apply(convnormrelu, "conv2_2", 128)
              .MaxPooling("pool2", 2)
              # 56
              .apply(convnormrelu, "conv3_1", 256)
              .apply(convnormrelu, "conv3_2", 256)
              .apply(convnormrelu, "conv3_3", 256)
              .MaxPooling("pool3", 2)
              # 28
              .apply(convnormrelu, "conv4_1", 512)
              .apply(convnormrelu, "conv4_2", 512)
              .apply(convnormrelu, "conv4_3", 512)
              .MaxPooling("pool4", 2)
              # 14
              .apply(convnormrelu, "conv5_1", 512)
              .apply(convnormrelu, "conv5_2", 512)
              .apply(convnormrelu, "conv5_3", 512)
              .MaxPooling("pool5", 2)
              # 7
              .FullyConnected(
                  "fc6",
                  4096,
                  kernel_initializer=tf.random_normal_initializer(
                      stddev=0.001
                  ),
              )
              .tf.nn.relu(name="fc6_relu")
              .Dropout("drop0", rate=0.5)
              .FullyConnected(
                  "fc7",
                  4096,
                  kernel_initializer=tf.random_normal_initializer(
                      stddev=0.001
                  ),
              )
              .tf.nn.relu(name="fc7_relu")
              .Dropout("drop1", rate=0.5)
              .FullyConnected(
                  "fc8",
                  1000,
                  kernel_initializer=tf.random_normal_initializer(
                      stddev=0.01
                  ),
              )()
          )
      add_param_summary((".*", ["histogram", "rms"]))
      return logits
  
file_path: tensorpack/examples/ImageNetModels/vgg16.py
imports: import argparse
  import os
  from tensorpack import tfv1
  from tensorpack import *
  from tensorpack.tfutils import argscope
  from tensorpack.tfutils.summary import *
  from tensorpack.utils.gpu import get_num_gpu
  from imagenet_utils import ImageNetModel, fbresnet_augmentor, 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