class_docstr: OpenLORIS Pytorch Dataset
code: def _load_metadata(self) -> bool:
      if not self._check_integrity():
          return False
  
      # any scenario and factor is good here since we want just to load the
      # train images and targets with no particular order
      scen = "domain"
      factor = [_ for _ in range(4)]
      ntask = 9
  
      print("Loading paths...")
      with open(str(self.root / "Paths.pkl"), "rb") as f:
          self.train_test_paths = pkl.load(f)
  
      print("Loading labels...")
      with open(str(self.root / "Labels.pkl"), "rb") as f:
          self.all_targets = pkl.load(f)
          self.train_test_targets = []
          for fact in factor:
              for i in range(ntask + 1):
                  self.train_test_targets += self.all_targets[scen][
                      fact
                  ][i]
  
      print("Loading LUP...")
      with open(str(self.root / "LUP.pkl"), "rb") as f:
          self.LUP = pkl.load(f)
  
      self.idx_list = []
      if self.train:
          for fact in factor:
              for i in range(ntask):
                  self.idx_list += self.LUP[scen][fact][i]
      else:
          for fact in factor:
              self.idx_list += self.LUP[scen][fact][-1]
  
      self.paths = []
      self.targets = []
  
      for idx in self.idx_list:
          self.paths.append(self.train_test_paths[idx])
          self.targets.append(self.train_test_targets[idx])
  
      return True
  
file_path: avalanche/avalanche/benchmarks/datasets/openloris/openloris.py
imports: import pickle
  from pathlib import Path
  from typing import Optional, Union
  from torchvision.datasets.folder import default_loader
  from torchvision.transforms import ToTensor
  from avalanche.benchmarks.datasets import DownloadableDataset, default_dataset_location
  from avalanche.benchmarks.datasets.openloris import openloris_data
label_desc: Use image node_id 3 for calling the OpenLORIS obj's underlying member method code with example usage: obj._load_metadata() and returns: True, False