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 __init__(
      self,
      root: Union[str, Path],
      split: str = "val",
      transform: Optional[Callable[[Image], Tensor]] = None,
  ):
      self.root: Path = Path(root)
  
      if split not in ("val",):
          raise ValueError("Invalid split.")
  
      self.split = self.split_map[split]
      self.transform = transform
  
      self.paths = sorted(
          [*self.root.joinpath(self.split).glob("*.png")]
      )
  
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 1 to create a new DIV2KDataset object from inherited base classes: Dataset with example: obj = DIV2KDataset(root, split, transform)