code: def CORe50(
      *,
      scenario: str = "nicv2_391",
      run: int = 0,
      object_lvl: bool = True,
      mini: bool = False,
      train_transform: Optional[Any] = _default_train_transform,
      eval_transform: Optional[Any] = _default_eval_transform,
      dataset_root: Optional[Union[str, Path]] = None,
  ):
      """
      Creates a CL benchmark for CORe50.
  
      If the dataset is not present in the computer, this method will
      automatically download and store it.
  
      This generator can be used to obtain the NI, NC, NIC and NICv2-* scenarios.
  
      The benchmark instance returned by this method will have two fields,
      `train_stream` and `test_stream`, which can be iterated to obtain
      training and test :class:`Experience`. Each Experience contains the
      `dataset` and the associated task label.
  
      The task label 0 will be assigned to each experience.
  
      The benchmark API is quite simple and is uniform across all benchmark
      generators. It is recommended to check the tutorial of the "benchmark" API,
      which contains usage examples ranging from "basic" to "advanced".
  
      :param scenario: CORe50 main scenario. It can be chosen between 'ni', 'nc',
          'nic', 'nicv2_79', 'nicv2_196' or 'nicv2_391.'
      :param run: number of run for the benchmark. Each run defines a different
          ordering. Must be a number between 0 and 9.
      :param object_lvl: True for a 50-way classification at the object level.
          False if you want to use the categories as classes. Default to True.
      :param mini: True for processing reduced 32x32 images instead of the
          original 128x128. Default to False.
      :param train_transform: The transformation to apply to the training data,
          e.g. a random crop, a normalization or a concatenation of different
          transformations (see torchvision.transform documentation for a
          comprehensive list of possible transformations). Defaults to None.
      :param eval_transform: The transformation to apply to the test data,
          e.g. a random crop, a normalization or a concatenation of different
          transformations (see torchvision.transform documentation for a
          comprehensive list of possible transformations). Defaults to None.
      :param dataset_root: Absolute path indicating where to store the dataset
          and related metadata. Defaults to None, which means that the default
          location for
          'core50' will be used.
  
      :returns: a properly initialized :class:`GenericCLScenario` instance.
      """
  
      assert 0 <= run <= 9, (
          "Pre-defined run of CORe50 are only 10. Indicate "
          "a number between 0 and 9."
      )
      assert scenario in nbatch.keys(), (
          "The selected scenario is note "
          "recognized: it should be 'ni', 'nc',"
          "'nic', 'nicv2_79', 'nicv2_196' or "
          "'nicv2_391'."
      )
  
      if dataset_root is None:
          dataset_root = default_dataset_location("core50")
  
      # Download the dataset and initialize filelists
      core_data = CORe50Dataset(root=dataset_root, mini=mini)
  
      root = core_data.root
      if mini:
          bp = "core50_32x32"
      else:
          bp = "core50_128x128"
      root_img = root / bp
  
      if object_lvl:
          suffix = "/"
      else:
          suffix = "_cat/"
      filelists_bp = (
          scen2dirs[scenario][:-1] + suffix + "run" + str(run)
      )
      train_failists_paths = []
      for batch_id in range(nbatch[scenario]):
          train_failists_paths.append(
              root
              / filelists_bp
              / (
                  "train_batch_"
                  + str(batch_id).zfill(2)
                  + "_filelist.txt"
              )
          )
  
      benchmark_obj = create_generic_benchmark_from_filelists(
          root_img,
          train_failists_paths,
          [root / filelists_bp / "test_filelist.txt"],
          task_labels=[0 for _ in range(nbatch[scenario])],
          complete_test_set_only=True,
          train_transform=train_transform,
          eval_transform=eval_transform,
      )
  
      if scenario == "nc":
          n_classes_per_exp = []
          classes_order = []
          for exp in benchmark_obj.train_stream:
              exp_dataset = exp.dataset
              unique_targets = list(
                  sorted(set(int(x) for x in exp_dataset.targets))  # type: ignore
              )
              n_classes_per_exp.append(len(unique_targets))
              classes_order.extend(unique_targets)
          setattr(benchmark_obj, "n_classes_per_exp", n_classes_per_exp)
          setattr(benchmark_obj, "classes_order", classes_order)
      setattr(benchmark_obj, "n_classes", 50 if object_lvl else 10)
  
      return benchmark_obj
  
file_path: avalanche/avalanche/benchmarks/classic/core50.py
imports: from pathlib import Path
  from typing import Union, Optional, Any
  from torchvision.transforms import ToTensor, Normalize, Compose, RandomHorizontalFlip
  from avalanche.benchmarks.classic.classic_benchmarks_utils import check_vision_benchmark
  from avalanche.benchmarks.datasets import default_dataset_location
  from avalanche.benchmarks.scenarios.deprecated.generic_benchmark_creation import create_generic_benchmark_from_filelists
  from avalanche.benchmarks.datasets.core50.core50 import CORe50Dataset
label_desc: Use image node_id 1 for calling a global function with example usage: CORe50() and returns: benchmark_obj