class_docstr: The Penn-Fudan Pedestrian detection and segmentation dataset
  
  Adapted from the "TorchVision Object Detection Finetuning Tutorial":
  https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html
code: def _load_metadata(self):
      # load all image files, sorting them to
      # ensure that they are aligned
      imgs = (self.root / "PennFudanPed" / "PNGImages").iterdir()
      masks = (self.root / "PennFudanPed" / "PedMasks").iterdir()
  
      self.imgs = list(sorted(imgs))
      self.masks = list(sorted(masks))
  
      self.targets = [
          self.make_targets(i) for i in range(len(self.imgs))
      ]
      return (
          Path(self.imgs[0]).exists() and Path(self.masks[0]).exists()
      )
  
file_path: avalanche/avalanche/benchmarks/datasets/penn_fudan/penn_fudan_dataset.py
imports: from pathlib import Path
  from typing import Dict, List, Optional, Sequence, Union
  import numpy
  import torch
  from PIL import Image
  from torchvision.datasets.folder import default_loader
  from avalanche.benchmarks.datasets import SimpleDownloadableDataset, default_dataset_location
  from avalanche.benchmarks.datasets.penn_fudan.penn_fudan_data import penn_fudan_data
label_desc: Use image node_id 2 for calling the PennFudanDataset obj's underlying member method code with example usage: obj._load_metadata() and returns: unknown