code: def _make_npz(path_npz, path_caffemodel, model, n_layers):
      sys.stderr.write(
          "Now loading caffemodel (usually it may take few minutes)\n"
      )
      sys.stderr.flush()
      if not os.path.exists(path_caffemodel):
          raise IOError(
              "The pre-trained caffemodel does not exist. Please download it "
              "from 'https://github.com/KaimingHe/deep-residual-networks', "
              "and place it on {}".format(path_caffemodel)
          )
      ResNetLayers.convert_caffemodel_to_npz(
          path_caffemodel, path_npz, n_layers
      )
      npz.load_npz(path_npz, model)
      return model
  
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 10 for calling a global function with example usage: _make_npz(path_npz, path_caffemodel, model, n_layers) and returns: model