code: def _transfer_resnet152(src, dst):
      dst.conv1.W.array[:] = src.conv1.W.array
      dst.bn1.avg_mean[:] = src.bn_conv1.avg_mean
      dst.bn1.avg_var[:] = src.bn_conv1.avg_var
      dst.bn1.gamma.array[:] = src.scale_conv1.W.array
      dst.bn1.beta.array[:] = src.scale_conv1.bias.b.array
  
      _transfer_block(src, dst.res2, ["2a", "2b", "2c"])
      _transfer_block(
          src,
          dst.res3,
          ["3a"] + ["3b{}".format(i) for i in range(1, 8)],
      )
      _transfer_block(
          src,
          dst.res4,
          ["4a"] + ["4b{}".format(i) for i in range(1, 36)],
      )
      _transfer_block(src, dst.res5, ["5a", "5b", "5c"])
  
      dst.fc6.W.array[:] = src.fc1000.W.array
      dst.fc6.b.array[:] = src.fc1000.b.array
  
imports: import collections
  import os
  import sys
  import warnings
  import numpy
  import chainer
  from chainer.dataset.convert import concat_examples
  from chainer.dataset import download
  from chainer import function
  from chainer.functions.activation.relu import relu
  from chainer.functions.activation.softmax import softmax
  from chainer.functions.array.reshape import reshape
  from chainer.functions.math.sum import sum
  from chainer.functions.pooling.average_pooling_2d import average_pooling_2d
  from chainer.functions.pooling.max_pooling_nd import max_pooling_2d
  from chainer.initializers import constant
  from chainer.initializers import normal
  from chainer import link
  from chainer.links.connection.convolution_2d import Convolution2D
  from chainer.links.connection.linear import Linear
  from chainer.links.normalization.batch_normalization import BatchNormalization
  from chainer.serializers import npz
  from chainer.utils import argument
  from chainer.utils import imgproc
  from chainer.variable import Variable
label_desc: Use image node_id 9 for calling a global function with example usage: _transfer_resnet152(src, dst) without return types