code: def get_logits(self, image):
      cfg = {
          18: ([2, 2, 2, 2], preresnet_basicblock),
          34: ([3, 4, 6, 3], preresnet_basicblock),
      }
      defs, block_func = cfg[DEPTH]
  
      with argscope(
          Conv2D,
          use_bias=False,
          kernel_initializer=tf.variance_scaling_initializer(
              scale=2.0, mode="fan_out"
          ),
      ), argscope(
          [Conv2D, MaxPooling, GlobalAvgPooling, BatchNorm],
          data_format="channels_first",
      ):
          convmaps = (
              LinearWrap(image)
              .Conv2D("conv0", 64, 7, strides=2, activation=BNReLU)
              .MaxPooling("pool0", 3, strides=2, padding="SAME")
              .apply2(
                  preresnet_group, "group0", block_func, 64, defs[0], 1
              )
              .apply2(
                  preresnet_group, "group1", block_func, 128, defs[1], 2
              )
              .apply2(
                  preresnet_group, "group2", block_func, 256, defs[2], 2
              )
              .apply2(
                  preresnet_group,
                  "group3new",
                  block_func,
                  512,
                  defs[3],
                  1,
              )()
          )
          print(convmaps)
          convmaps = GlobalAvgPooling("gap", convmaps)
          logits = FullyConnected("linearnew", convmaps, 1000)
      return logits
  
imports: import argparse
  import multiprocessing
  import numpy
  import os
  import sys
  import cv2
  import tensorflow
  from tensorpack import *
  from tensorpack.dataflow import dataset
  from tensorpack.tfutils import gradproc, optimizer
  from tensorpack.tfutils.summary import *
  from tensorpack.utils import viz
  from tensorpack.utils.gpu import get_num_gpu
  from imagenet_utils import ImageNetModel, fbresnet_augmentor
  from resnet_model import preresnet_basicblock, preresnet_group
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