code: def _retrieve(n_layers, name_npz, name_caffemodel, model):
      root = download.get_dataset_directory("pfnet/chainer/models/")
      path = os.path.join(root, name_npz)
      path_caffemodel = os.path.join(root, name_caffemodel)
      return download.cache_or_load_file(
          path,
          lambda path: _make_npz(
              path, path_caffemodel, model, n_layers
          ),
          lambda path: npz.load_npz(path, 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 11 for calling a global function with example usage: _retrieve(n_layers, name_npz, name_caffemodel, model) and returns: download