code: def get_example(self, i):
      # It reads the i-th image/label pair and return a preprocessed image.
      # It applies following preprocesses:
      #     - Cropping (random or center rectangular)
      #     - Random flip
      #     - Scaling to [0, 1] value
      crop_size = self.crop_size
  
      image, label = self.base[i]
      _, h, w = image.shape
  
      if self.random:
          # Randomly crop a region and flip the image
          top = random.randint(0, h - crop_size - 1)
          left = random.randint(0, w - crop_size - 1)
          if random.randint(0, 1):
              image = image[:, :, ::-1]
      else:
          # Crop the center
          top = (h - crop_size) // 2
          left = (w - crop_size) // 2
      bottom = top + crop_size
      right = left + crop_size
  
      image = image[:, top:bottom, left:right]
      image -= self.mean[:, top:bottom, left:right]
      image *= 1.0 / 255.0  # Scale to [0, 1]
      return image, label
  
imports: from __future__ import print_function
  import argparse
  import multiprocessing
  import random
  import sys
  import numpy
  import chainer
  import chainer.cuda
  from chainer import training
  from chainer.training import extensions
  import chainermn
  import models.alex
  import models.googlenet
  import models.googlenetbn
  import models.nin
  import models.resnet50
label_desc: Use image node_id 3 for calling the PreprocessedDataset obj's underlying member method code with example usage: obj.get_example(i) and returns: image,  label