class_docstr: CORe50 Pytorch Dataset
class_docstr_tok: ["CORe50","Pytorch","Dataset"]
docstr: Mapping an object label into its corresponding category label
  based on the scenario.
docstr_tok: ["Mapping","an","object","label","into","its","corresponding","category","label","based","on","the","scenario","."]
code: def _objlab2cat(self, label, scen, run):
      """Mapping an object label into its corresponding category label
      based on the scenario."""
  
      if scen == "nc":
          return core50_data.name2cat[
              self.labels2names["nc"][run][label][:-1]
          ]
      else:
          return int(label) // 5
  
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 8 for calling the CORe50Dataset obj's underlying member method code with example usage: obj._objlab2cat(label, scen, run) and returns: core50_data, unknown