class_docstr: Dataloader for DIV2K Dataset.
  
  The DIV2K dataset was released for the 2017 NTIRE challenge on single-image
  super resolution. The dataset is documented in the following paper:
  
  NTIRE 2017 challenge on single image super-resolution: Dataset and study
  E Agustsson, R Timofte
  
  Args:
      root: Root directory of dataset.
      split: Split of the dataset (currently only 'val' is accepted).
      transform: Torchvision PIL transform for converting the images.
code: def __getitem__(self, index: int) -> Image:
      path = self.paths[index]
  
      image = torchvision.datasets.folder.default_loader(path)
  
      if self.transform is not None:
          image = self.transform(image)
  
      return image
  
file_path: NeuralCompression/neuralcompression/data/_div2k_dataset.py
imports: from pathlib import Path
  from typing import Callable, Optional, Union
  import torchvision.datasets.folder
  import torchvision.datasets.utils
  from PIL.Image import Image
  from torch import Tensor
  from torch.utils.data import Dataset
label_desc: Use image node_id 2 for calling the DIV2KDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: image