class_docstr: Dataset that supports Tar archives (uncompressed).
  
  Args:
    archive (string or TarDataset): Path to the Tar file containing the dataset.
      Alternatively, pass in a TarDataset object to reuse its cached information;
      this is useful for loading different subsets within the same archive.
    extensions (tuple): Extensions (strings starting with a dot), only files
      with these extensions will be iterated. Default: png/jpg/jpeg.
    is_valid_file (callable): Optional function that takes file information as
      input (tarfile.TarInfo) and outputs True for files that need to be
      iterated; overrides extensions argument.
      Example: lambda m: m.isfile() and m.name.endswith('.png')
    transform (callable): Function applied to each image by __getitem__ (see
      torchvision.transforms). Default: ToTensor (convert PIL image to tensor).
  
  Attributes:
    members_by_name (dict): Members (files and folders) found in the Tar archive,
      with their names as keys and their tarfile.TarInfo structures as values.
    samples (list): Items to iterate (can be ignored by overriding __getitem__
      and __len__).
  
  Author: Joao F. Henriques
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docstr: Serialize without the TarFile references, for multiprocessing compatibility.
docstr_tok: ["Serialize","without","the","TarFile","references",",","for","multiprocessing","compatibility","."]
code: def __getstate__(self):
      """Serialize without the TarFile references, for multiprocessing compatibility."""
      state = dict(self.__dict__)
      state["tar_obj"] = {}
      return state
  
file_path: zoo/src/datamodules/datasets/tardataset.py
imports: import tarfile
  from io import BytesIO
  from PIL import Image, ImageFile
  from torch.utils.data import Dataset, get_worker_info
label_desc: Use image node_id 9 for calling the TarDataset obj's underlying member method code with example usage: obj.__getstate__() and returns: state