code: def name_conversion(caffe_layer_name):
      """Convert a caffe parameter name to a tensorflow parameter name as
      defined in the above model"""
      # beginning & end mapping
      NAME_MAP = {
          "bn_conv1/beta": "conv0/bn/beta",
          "bn_conv1/gamma": "conv0/bn/gamma",
          "bn_conv1/mean/EMA": "conv0/bn/mean/EMA",
          "bn_conv1/variance/EMA": "conv0/bn/variance/EMA",
          "conv1/W": "conv0/W",
          "conv1/b": "conv0/b",
          "fc1000/W": "linear/W",
          "fc1000/b": "linear/b",
      }
      if caffe_layer_name in NAME_MAP:
          return NAME_MAP[caffe_layer_name]
  
      s = re.search("([a-z]+)([0-9]+)([a-z]+)_", caffe_layer_name)
      if s is None:
          s = re.search(
              "([a-z]+)([0-9]+)([a-z]+)([0-9]+)_", caffe_layer_name
          )
          layer_block_part1 = s.group(3)
          layer_block_part2 = s.group(4)
          assert layer_block_part1 in ["a", "b"]
          layer_block = (
              0 if layer_block_part1 == "a" else int(layer_block_part2)
          )
      else:
          layer_block = ord(s.group(3)) - ord("a")
      layer_type = s.group(1)
      layer_group = s.group(2)
  
      layer_branch = int(
          re.search("_branch([0-9])", caffe_layer_name).group(1)
      )
      assert layer_branch in [1, 2]
      if layer_branch == 2:
          layer_id = re.search(
              "_branch[0-9]([a-z])/", caffe_layer_name
          ).group(1)
          layer_id = ord(layer_id) - ord("a") + 1
  
      TYPE_DICT = {"res": "conv{}", "bn": "conv{}/bn"}
      layer_type = TYPE_DICT[layer_type].format(
          layer_id if layer_branch == 2 else "shortcut"
      )
  
      tf_name = caffe_layer_name[caffe_layer_name.index("/") :]
      tf_name = (
          "group{}/block{}/{}".format(
              int(layer_group) - 2, layer_block, layer_type
          )
          + tf_name
      )
      return tf_name
  
file_path: tensorpack/examples/ResNet/load-resnet.py
imports: import argparse
  import functools
  import numpy
  import re
  import cv2
  import six
  import tensorflow
  from tensorpack import *
  from tensorpack.dataflow.dataset import ILSVRCMeta
  from tensorpack.utils import logger
  from imagenet_utils import ImageNetModel, eval_classification, get_imagenet_dataflow
  from resnet_model import resnet_bottleneck, resnet_group
label_desc: Use image node_id 3 for calling a global function with example usage: name_conversion(caffe_layer_name) and returns: tf_name, NAME_MAP