class_docstr: CORe50 Pytorch Dataset
class_docstr_tok: ["CORe50","Pytorch","Dataset"]
code: def _load_metadata(self) -> bool:
      if self.mini:
          bp = "core50_32x32"
      else:
          bp = "core50_128x128"
  
      if not (self.root / bp).exists():
          return False
  
      if not (self.root / "batches_filelists").exists():
          return False
  
      with open(self.root / "paths.pkl", "rb") as f:
          self.train_test_paths = pkl.load(f)
  
      if self.verbose:
          print("Loading labels...")
      with open(self.root / "labels.pkl", "rb") as f:
          self.all_targets = pkl.load(f)
          self.train_test_targets = []
          for i in range(self._nbatch + 1):
              self.train_test_targets += self.all_targets[self._scen][
                  self._run
              ][i]
  
      if self.verbose:
          print("Loading LUP...")
      with open(self.root / "LUP.pkl", "rb") as f:
          self.LUP = pkl.load(f)
  
      if self.verbose:
          print("Loading labels names...")
      with open(self.root / "labels2names.pkl", "rb") as f:
          self.labels2names = pkl.load(f)
  
      self.idx_list = []
      if self.train:
          for i in range(self._nbatch):
              self.idx_list += self.LUP[self._scen][self._run][i]
      else:
          self.idx_list = self.LUP[self._scen][self._run][-1]
  
      self.paths = []
      self.targets = []
  
      for idx in self.idx_list:
          self.paths.append(self.train_test_paths[idx])
          div = 1
          if not self.object_level:
              div = 5
          self.targets.append(self.train_test_targets[idx] // div)
  
      with open(self.root / "labels2names.pkl", "rb") as f:
          self.labels2names = pkl.load(f)
  
      if not (self.root / "NIC_v2_79_cat").exists():
          self._create_cat_filelists()
  
      return True
  
imports: import glob
  import os
  import pickle
  from pathlib import Path
  from typing import List, Optional, Tuple, Union
  from warnings import warn
  from torchvision.datasets.folder import default_loader
  from torchvision.transforms import ToTensor
  from avalanche.benchmarks.datasets.core50 import core50_data
  from avalanche.benchmarks.datasets import default_dataset_location
  from avalanche.benchmarks.datasets.downloadable_dataset import DownloadableDataset
label_desc: Use image node_id 5 for calling the CORe50Dataset obj's underlying member method code with example usage: obj._load_metadata() and returns: True, False, False