code: def get_weightmap(model):
      weightmap = {}
      if model == "xception65":
          weightmap[("feature_extractor", "entryflow_conv1")] = (
              "Conv2DBNActiv",
              "deeplab/xception_65/entry_flow/conv1_1",
          )
          weightmap[("feature_extractor", "entryflow_conv2")] = (
              "Conv2DBNActiv",
              "deeplab/xception_65/entry_flow/conv1_2",
          )
          weightmap[("feature_extractor", "entryflow_block1")] = (
              "XceptionBlock",
              "deeplab/xception_65/entry_flow/block1/unit_1",
          )
          weightmap[("feature_extractor", "entryflow_block2")] = (
              "XceptionBlock",
              "deeplab/xception_65/entry_flow/block2/unit_1",
          )
          weightmap[("feature_extractor", "entryflow_block3")] = (
              "XceptionBlock",
              "deeplab/xception_65/entry_flow/block3/unit_1",
          )
          for i in range(1, 17):
              weightmap[
                  ("feature_extractor", "middleflow_block{}".format(i))
              ] = (
                  "XceptionBlock",
                  "deeplab/xception_65/middle_flow/block1/unit_{}".format(
                      i
                  ),
              )
          weightmap[("feature_extractor", "exitflow_block1")] = (
              "XceptionBlock",
              "deeplab/xception_65/exit_flow/block1/unit_1",
          )
          weightmap[("feature_extractor", "exitflow_block2")] = (
              "XceptionBlock",
              "deeplab/xception_65/exit_flow/block2/unit_1",
          )
  
          weightmap[("aspp", "image_pooling_conv")] = (
              "Conv2DBNActiv",
              "deeplab/image_pooling",
          )
          weightmap[("aspp", "conv1x1")] = (
              "Conv2DBNActiv",
              "deeplab/aspp0",
          )
          weightmap[("aspp", "atrous1")] = (
              "SeparableConv2DBNActiv",
              "deeplab/aspp1",
          )
          weightmap[("aspp", "atrous2")] = (
              "SeparableConv2DBNActiv",
              "deeplab/aspp2",
          )
          weightmap[("aspp", "atrous3")] = (
              "SeparableConv2DBNActiv",
              "deeplab/aspp3",
          )
          weightmap[("aspp", "proj")] = (
              "Conv2DBNActiv",
              "deeplab/concat_projection",
          )
  
          weightmap[("decoder", "feature_proj")] = (
              "Conv2DBNActiv",
              "deeplab/decoder/feature_projection0",
          )
          weightmap[("decoder", "conv1")] = (
              "SeparableConv2DBNActiv",
              "deeplab/decoder/decoder_conv0",
          )
          weightmap[("decoder", "conv2")] = (
              "SeparableConv2DBNActiv",
              "deeplab/decoder/decoder_conv1",
          )
          weightmap[("decoder", "conv_logits")] = (
              "Convolution2D",
              "deeplab/logits/semantic",
          )
      else:
          raise
  
      return weightmap
  
file_path: chainercv/examples/deeplab/tf2npz.py
imports: import argparse
  import chainer
  from chainercv.datasets import ade20k_semantic_segmentation_label_names
  from chainercv.datasets import cityscapes_semantic_segmentation_label_names
  from chainercv.datasets import voc_semantic_segmentation_label_names
  from chainercv.links import DeepLabV3plusXception65
  import tensorflow
label_desc: Use image node_id 4 for calling a global function with example usage: get_weightmap(model) and returns: weightmap