code: def __call__(self, in_data):
      # There are five data augmentation steps
      # 1. Color augmentation
      # 2. Random expansion
      # 3. Random cropping
      # 4. Resizing with random interpolation
      # 5. Random horizontal flipping
  
      img, bbox, label = in_data
  
      # 1. Color augmentation
      img = random_distort(img)
  
      # 2. Random expansion
      if np.random.randint(2):
          img, param = transforms.random_expand(
              img, fill=self.mean, return_param=True
          )
          bbox = transforms.translate_bbox(
              bbox,
              y_offset=param["y_offset"],
              x_offset=param["x_offset"],
          )
  
      # 3. Random cropping
      img, param = random_crop_with_bbox_constraints(
          img, bbox, return_param=True
      )
      bbox, param = transforms.crop_bbox(
          bbox,
          y_slice=param["y_slice"],
          x_slice=param["x_slice"],
          allow_outside_center=False,
          return_param=True,
      )
      label = label[param["index"]]
  
      # 4. Resizing with random interpolatation
      _, H, W = img.shape
      img = resize_with_random_interpolation(
          img, (self.size, self.size)
      )
      bbox = transforms.resize_bbox(
          bbox, (H, W), (self.size, self.size)
      )
  
      # 5. Random horizontal flipping
      img, params = transforms.random_flip(
          img, x_random=True, return_param=True
      )
      bbox = transforms.flip_bbox(
          bbox, (self.size, self.size), x_flip=params["x_flip"]
      )
  
      # Preparation for SSD network
      img -= self.mean
      mb_loc, mb_label = self.coder.encode(bbox, label)
  
      return img, mb_loc, mb_label
  
imports: import argparse
  import copy
  import numpy
  import chainer
  from chainer.datasets import ConcatenatedDataset
  from chainer.datasets import TransformDataset
  from chainer.optimizer_hooks import WeightDecay
  from chainer import serializers
  from chainer import training
  from chainer.training import extensions
  from chainer.training import triggers
  from chainercv.datasets import voc_bbox_label_names
  from chainercv.datasets import VOCBboxDataset
  from chainercv.extensions import DetectionVOCEvaluator
  from chainercv.links.model.ssd import GradientScaling
  from chainercv.links.model.ssd import multibox_loss
  from chainercv.links import SSD300
  from chainercv.links import SSD512
  from chainercv import transforms
  from chainercv.links.model.ssd import random_crop_with_bbox_constraints
  from chainercv.links.model.ssd import random_distort
  from chainercv.links.model.ssd import resize_with_random_interpolation
  import cv2
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,  mb_loc,  mb_label