code: def converter(batch, device=None):
      # do not send data to gpu (device is ignored)
      return tuple(list(v) for v in zip(*batch))
  
imports: from __future__ import division
  import argparse
  import multiprocessing
  import numpy
  import PIL
  import chainer
  import chainer.functions
  import chainer.links
  from chainer.optimizer_hooks import WeightDecay
  from chainer import serializers
  from chainer import training
  from chainer.training import extensions
  import chainermn
  from chainercv.chainer_experimental.datasets.sliceable import TransformDataset
  from chainercv.chainer_experimental.training.extensions import make_shift
  from chainercv.links.model.fpn.misc import scale_img
  from chainercv import transforms
  from chainercv.datasets import coco_instance_segmentation_label_names
  from chainercv.datasets import COCOInstanceSegmentationDataset
  from chainercv.links import MaskRCNNFPNResNet101
  from chainercv.links import MaskRCNNFPNResNet50
  from chainercv.datasets import coco_bbox_label_names
  from chainercv.datasets import COCOBboxDataset
  from chainercv.links import FasterRCNNFPNResNet101
  from chainercv.links import FasterRCNNFPNResNet50
  from chainercv.links.model.fpn import bbox_head_loss_post
  from chainercv.links.model.fpn import bbox_head_loss_pre
  from chainercv.links.model.fpn import mask_head_loss_post
  from chainercv.links.model.fpn import mask_head_loss_pre
  from chainercv.links.model.fpn import rpn_loss
label_desc: Use image node_id 1 for calling a global function with example usage: converter(batch, device) and returns: tuple