code: def __getitem__(self, index):
      image, _ = self.dataset[index]
      img = image.convert("YCbCr")
      hr_image, _, _ = img.split()
      hr_image = center_crop(hr_image, self.crop_size)
      lr_image = hr_image.copy()
      if self.scale_factor != 1:
          size = self.crop_size // self.scale_factor
          lr_image = resize(lr_image, [size, size])
      hr_image = to_tensor(hr_image)
      lr_image = to_tensor(lr_image)
      return lr_image, hr_image
  
imports: import argparse
  import torch
  import torch.nn
  import torch.optim
  import torchvision
  from model import Net
  from torch.utils.data import DataLoader
  from torchvision.transforms.functional import center_crop, resize, to_tensor
  from ignite.contrib.handlers import ProgressBar
  from ignite.engine import Engine, Events
  from ignite.handlers import BasicTimeProfiler
  from ignite.metrics import PSNR
label_desc: Use image node_id 2 for calling the SRDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: lr_image,  hr_image