code: def _transfer_components(src, dst_conv, dst_bn, bname, cname):
      src_conv = getattr(src, "conv{}_{}".format(bname, cname))
      src_bn = getattr(src, "conv{}_{}/bn".format(bname, cname))
      src_scale = getattr(
          src, "conv{}_{}/bn/scale".format(bname, cname)
      )
      if dst_conv.groups == 1:
          dst_conv.W.data[:] = src_conv.W.data
      else:
          group_size = src_conv.W.data.shape[1] // dst_conv.groups
          for group in range(dst_conv.groups):
              from_idx = group_size * group
              to_idx = group_size * (group + 1)
              dst_conv.W.data[
                  from_idx:to_idx, :, :, :
              ] = src_conv.W.data[
                  from_idx:to_idx, from_idx:to_idx, :, :
              ]
      dst_bn.avg_mean[:] = src_bn.avg_mean
      dst_bn.avg_var[:] = src_bn.avg_var
      dst_bn.gamma.data[:] = src_scale.W.data
      dst_bn.beta.data[:] = src_scale.bias.b.data
  
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 1 for calling a global function with example usage: _transfer_components(src, dst_conv, dst_bn, bname, cname) without return types