class_docstr: `LFW <http://vis-www.cs.umass.edu/lfw/>`_ Dataset.
  
  Args:
      root (string): Root directory of dataset where directory
          ``lfw-py`` exists or will be saved to if download is set to True.
      split (string, optional): The image split to use. Can be one of ``train``, ``test``,
          ``10fold`` (default).
      image_set (str, optional): Type of image funneling to use, ``original``, ``funneled`` or
          ``deepfunneled``. Defaults to ``funneled``.
      transform (callable, optional): A function/transform that  takes in a PIL image
          and returns a transformed version. E.g, ``transforms.RandomRotation``
      target_transform (callable, optional): A function/transform that takes in the
          target and transforms it.
      download (bool, optional): If true, downloads the dataset from the internet and
          puts it in root directory. If dataset is already downloaded, it is not
          downloaded again.
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code: def _get_people(self) -> Tuple[List[str], List[int]]:
      data, targets = [], []
      with open(os.path.join(self.root, self.labels_file)) as f:
          lines = f.readlines()
          n_folds, s = (
              (int(lines[0]), 1) if self.split == "10fold" else (1, 0)
          )
  
          for fold in range(n_folds):
              n_lines = int(lines[s])
              people = [
                  line.strip().split("\t")
                  for line in lines[s + 1 : s + n_lines + 1]
              ]
              s += n_lines + 1
              for i, (identity, num_imgs) in enumerate(people):
                  for num in range(1, int(num_imgs) + 1):
                      img = self._get_path(identity, num)
                      data.append(img)
                      targets.append(self.class_to_idx[identity])
  
      return data, targets
  
imports: import os
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  from PIL import Image
  from .utils import check_integrity, download_and_extract_archive, download_url, verify_str_arg
  from .vision import VisionDataset
label_desc: Use image node_id 2 for calling the LFWPeople obj's underlying member method code with example usage: obj._get_people() and returns: data,  targets