code: def __call__(self, in_data):
      if len(in_data) == 4:
          img, mask, label, bbox = in_data
      else:
          img, bbox, label = in_data
      # Flipping
      img, params = transforms.random_flip(
          img, x_random=True, return_param=True
      )
      x_flip = params["x_flip"]
      bbox = transforms.flip_bbox(bbox, img.shape[1:], x_flip=x_flip)
  
      # Scaling and mean subtraction
      img, scale = scale_img(img, self.min_size, self.max_size)
      img -= self.mean
      bbox = bbox * scale
  
      if len(in_data) == 4:
          mask = transforms.flip(mask, x_flip=x_flip)
          mask = transforms.resize(
              mask.astype(np.float32),
              img.shape[1:],
              interpolation=PIL.Image.NEAREST,
          ).astype(np.bool)
          return img, bbox, label, mask
      else:
          return img, bbox, label
  
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 2 for calling the Transform obj's underlying member method code with example usage: obj.__call__(in_data) and returns: img,  bbox,  label,  mask, img,  bbox,  label