class_docstr: This class extends the basic Pytorch Dataset class to handle list of paths
  as the main data source.
docstr: Returns next element in the dataset given the current index.
  
  :param index: index of the data to get.
  :return: loaded item.
docstr_tok: ["Returns","next","element","in","the","dataset","given","the","current","index",".",":","param","index",":","index","of","the","data","to","get",".",":","return",":","loaded","item","."]
code: def __getitem__(self, index):
      """
      Returns next element in the dataset given the current index.
  
      :param index: index of the data to get.
      :return: loaded item.
      """
  
      img_description = self.imgs[index]
      impath = img_description[0]
      target = img_description[1]
      bbox = None
      if len(img_description) > 2:
          bbox = img_description[2]
  
      if self.root is not None:
          impath = self.root / impath
      img = self.loader(impath)
  
      # If a bounding box is provided, crop the image before passing it to
      # any user-defined transformation.
      if bbox is not None:
          if isinstance(bbox, Tensor):
              bbox = bbox.tolist()
          # crop accepts PIL images, too
          img = crop(img, *bbox)  # type: ignore
  
      if self.transform is not None:
          img = self.transform(img)
      if self.target_transform is not None:
          target = self.target_transform(target)
  
      return img, target
  
imports: from pathlib import Path
  from typing import Callable, Generic, List, Tuple, Sequence, Optional, TypeVar, Union
  import torch.utils.data
  from PIL import Image
  import os
  import os.path
  from torch import Tensor
  from torchvision.transforms.functional import crop
  from .transform_groups import XTransform, YTransform
label_desc: Use image node_id 2 for calling the PathsDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: img,  target