code: def __iter__(self) -> Iterator[Tuple[str, Dict[str, str]]]:
      for _, file in self.datapipe:
          try:
              lines = (line.decode() for line in file)
  
              if self.fieldnames:
                  fieldnames = self.fieldnames
              else:
                  # The first row is skipped, because it only contains the number of samples
                  next(lines)
  
                  # Empty field names are filtered out, because some files have an extra white space after the header
                  # line, which is recognized as extra column
                  fieldnames = [
                      name
                      for name in next(
                          csv.reader([next(lines)], dialect="celeba")
                      )
                      if name
                  ]
                  # Some files do not include a label for the image ID column
                  if fieldnames[0] != "image_id":
                      fieldnames.insert(0, "image_id")
  
              for line in csv.DictReader(
                  lines, fieldnames=fieldnames, dialect="celeba"
              ):
                  yield line.pop("image_id"), line
          finally:
              file.close()
  
imports: import csv
  import pathlib
  from typing import Any, BinaryIO, Dict, Iterator, List, Optional, Sequence, Tuple, Union
  import torch
  from torchdata.datapipes.iter import Filter, IterDataPipe, IterKeyZipper, Mapper, Zipper
  from torchvision.prototype.datasets.utils import Dataset, EncodedImage, GDriveResource, OnlineResource
  from torchvision.prototype.datasets.utils._internal import getitem, hint_sharding, hint_shuffling, INFINITE_BUFFER_SIZE, path_accessor
  from torchvision.prototype.tv_tensors import Label
  from torchvision.tv_tensors import BoundingBoxes
  from .._api import register_dataset, register_info
label_desc: Use image node_id 2 for calling the CelebACSVParser obj's underlying member method code with example usage: obj.__iter__() without return types