code: def _depth_multiplied_output_channels(
      base_out_channels, multiplier, divisable_by=8, min_depth=8
  ):
      return _make_divisible(
          base_out_channels * multiplier, divisable_by, min_depth
      )
  
file_path: chainercv/chainercv/links/model/mobilenet/mobilenet_v2.py
imports: import numpy
  import chainer
  from chainer.functions import average_pooling_2d
  from chainer.functions import clipped_relu
  from chainer.functions import softmax
  from chainer.functions import squeeze
  from chainercv.links.model.mobilenet.expanded_conv_2d import ExpandedConv2D
  from chainercv.links.model.mobilenet.tf_conv_2d_bn_activ import TFConv2DBNActiv
  from chainercv.links.model.mobilenet.tf_convolution_2d import TFConvolution2D
  from chainercv.links.model.mobilenet.util import _make_divisible
  from chainercv.links.model.mobilenet.util import expand_input_by_factor
  from chainercv.links.model.pickable_sequential_chain import PickableSequentialChain
  from chainercv import utils
label_desc: Use image node_id 1 for calling a global function with example usage: _depth_multiplied_output_channels(base_out_channels, multiplier, divisable_by, min_depth) and returns: _make_divisible