code: def __call__(self, in_data):
      img, mask, label = in_data
      bbox = mask_to_bbox(mask)
      _, orig_H, orig_W = img.shape
      img = self.fcis.prepare(img)
      _, H, W = img.shape
      scale = H / orig_H
      mask = transforms.resize(mask.astype(np.float32), (H, W))
      bbox = transforms.resize_bbox(bbox, (orig_H, orig_W), (H, W))
  
      img, params = transforms.random_flip(
          img, x_random=True, return_param=True
      )
      mask = transforms.flip(mask, x_flip=params["x_flip"])
      bbox = transforms.flip_bbox(bbox, (H, W), x_flip=params["x_flip"])
      return img, mask, label, bbox, scale
  
imports: from __future__ import division
  import argparse
  import numpy
  import six
  import chainer
  from chainer.dataset.convert import _concat_arrays
  from chainer.dataset.convert import to_device
  from chainer.datasets import TransformDataset
  from chainer.training import extensions
  from chainer.training.triggers import ManualScheduleTrigger
  from chainercv.datasets import sbd_instance_segmentation_label_names
  from chainercv.datasets import SBDInstanceSegmentationDataset
  from chainercv.experimental.links import FCISResNet101
  from chainercv.experimental.links import FCISTrainChain
  from chainercv.extensions import InstanceSegmentationVOCEvaluator
  from chainercv.links.model.ssd import GradientScaling
  from chainercv import transforms
  from chainercv.utils.mask.mask_to_bbox import mask_to_bbox
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,  mask,  label,  bbox,  scale