code: def resolve(weightmap):
      # resolve weightmap
      changed = True
      while changed:
          changed = False
          for key in list(weightmap.keys()):
              layer, op = weightmap.pop(key)
              if layer == "Conv2DBNActiv":
                  weightmap[key + ("conv",)] = ("Convolution2D", op)
                  weightmap[key + ("bn",)] = (
                      "BatchNormalization",
                      op + "/BatchNorm",
                  )
                  changed = True
              elif layer == "SeparableConv2DBNActiv":
                  weightmap[key + ("depthwise",)] = (
                      "Convolution2D_depthwise",
                      op + "_depthwise",
                  )
                  weightmap[key + ("dw_bn",)] = (
                      "BatchNormalization",
                      op + "_depthwise/BatchNorm",
                  )
                  weightmap[key + ("pointwise",)] = (
                      "Convolution2D",
                      op + "_pointwise",
                  )
                  weightmap[key + ("pw_bn",)] = (
                      "BatchNormalization",
                      op + "_pointwise/BatchNorm",
                  )
                  changed = True
              elif layer == "XceptionBlock":
                  weightmap[key + ("separable1",)] = (
                      "SeparableConv2DBNActiv",
                      op + "/xception_module/separable_conv1",
                  )
                  weightmap[key + ("separable2",)] = (
                      "SeparableConv2DBNActiv",
                      op + "/xception_module/separable_conv2",
                  )
                  weightmap[key + ("separable3",)] = (
                      "SeparableConv2DBNActiv",
                      op + "/xception_module/separable_conv3",
                  )
                  weightmap[key + ("conv",)] = (
                      "Conv2DBNActiv",
                      op + "/xception_module/shortcut",
                  )
                  changed = True
              else:
                  weightmap[key] = (layer, op)
      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 5 for calling a global function with example usage: resolve(weightmap) and returns: weightmap