code: def _transfer_resnet50(src, dst, class_indices):
      # Reorder weights to work on RGB and not on BGR
      dst.conv1.conv.W.data[:] = src["conv1/7x7_s2"].W.data[:, ::-1]
      # No bias setting for conv1, which is different from ResNet50.
      dst.conv1.bn.avg_mean[:] = src["conv1/7x7_s2/bn"].avg_mean
      dst.conv1.bn.avg_var[:] = src["conv1/7x7_s2/bn"].avg_var
      dst.conv1.bn.gamma.data[:] = src["conv1/7x7_s2/bn/scale"].W.data
      dst.conv1.bn.beta.data[:] = src[
          "conv1/7x7_s2/bn/scale"
      ].bias.b.data
  
      _transfer_block(src, dst.res2, ["2_1", "2_2", "2_3"])
      _transfer_block(src, dst.res3, ["3_1", "3_2", "3_3", "3_4"])
      _transfer_block(
          src, dst.res4, ["4_1", "4_2", "4_3", "4_4", "4_5", "4_6"]
      )
      _transfer_block(src, dst.res5, ["5_1", "5_2", "5_3"])
  
      dst.fc6.W.data[:] = src.classifier.W.data[class_indices, :]
      dst.fc6.b.data[:] = src.classifier.b.data[class_indices]
  
imports: import argparse
  import numpy
  import os
  import chainer
  from chainer.links.caffe.caffe_function import CaffeFunction
  from chainercv.links import SEResNet101
  from chainercv.links import SEResNet152
  from chainercv.links import SEResNet50
  from chainercv.links import SEResNeXt101
  from chainercv.links import SEResNeXt50
label_desc: Use image node_id 6 for calling a global function with example usage: _transfer_resnet50(src, dst, class_indices) without return types