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
  
file_path: chainer/chainerx_cc/examples/imagenet_py/image_dataset.py
imports: import random
  import chainer
  import numpy
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