class_docstr: Open Images V6 data loader
  
  This is a minimalist dataloader for Open Images V6. Since Open Images puts
  all data into a single folder, it is expected that the user has already
  created a text file with all the images of the split prior to instantiating
  this class (otherwise an ``os.walk`` takes forever). The train file should
  be called 'list_train_files.txt' by default, for example.
  
  If the user gives a different name to the image list file, the new filename
  can be specified in the ``__init__`` function.
  
  Args:
      root: Root direcotry for Open Images V6.
      split: Which split of the dataset (one of 'train', 'val', or 'test').
          This is used to indicate the subfolder of ``root`` where the images
          are stored, except for the case of 'val', which is renamed to
          'validation'
      image_list_file: The file containing all image names under the split.
          By default, it assumes the file has the name
          'list-{split}-files.txt'.
      transform: A torchvision PIL transform.
code: def _get_image_path(self, index: int) -> Path:
      fname = self.indices[index]
      return self.root / self.split / (fname + ".jpg")
  
file_path: NeuralCompression/neuralcompression/data/_openimages_v6.py
imports: import os
  from pathlib import Path
  from typing import Callable, List, Optional, Union
  from torch.utils.data import Dataset
  from torchvision.datasets.folder import default_loader
label_desc: Use image node_id 2 for calling the OpenImagesV6 obj's underlying member method code with example usage: obj._get_image_path(index) and returns: unknown