code: def SplitCIFAR110(
      n_experiences: int,
      *,
      seed: Optional[int] = None,
      fixed_class_order: Optional[Sequence[int]] = None,
      class_ids_from_zero_from_first_exp: bool = False,
      train_transform: Optional[
          Any
      ] = _default_cifar100_train_transform,
      eval_transform: Optional[Any] = _default_cifar100_eval_transform,
      dataset_root_cifar10: Optional[Union[str, Path]] = None,
      dataset_root_cifar100: Optional[Union[str, Path]] = None,
  ) -> NCScenario:
      """
      Creates a CL benchmark using both the CIFAR100 and CIFAR10 datasets.
  
      If the datasets are not present in the computer, this method will
      automatically download and store them in the data folder.
  
      The CIFAR10 dataset is used to create the first experience, while the
      remaining `n_experiences-1` experiences will be created from CIFAR100.
  
      The returned benchmark will return experiences containing all patterns of a
      subset of classes, which means that each class is only seen "once".
      This is one of the most common scenarios in the Continual Learning
      literature. Common names used in literature to describe this kind of
      scenario are "Class Incremental", "New Classes", etc. By default,
      an equal amount of classes will be assigned to each experience.
  
      This generator will apply a task label 0 to all experiences.
  
      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 (always 0 for this specific
      benchmark).
  
      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 n_experiences: The number of experiences for the entire benchmark.
          The first experience will contain the entire CIFAR10 dataset, while the
          other n-1 experiences will be obtained from CIFAR100.
      :param seed: A valid int used to initialize the random number generator.
          Can be None.
      :param fixed_class_order: A list of class IDs used to define the class
          order ONLY for the incremental part, which is based on cifar100. The
          classes must be in range 0-99.
          If None, value of ``seed`` will be used to define the class order for
          the incremental batches on cifar100. If non-None, ``seed`` parameter
          will be ignored. Defaults to None.
      :param class_ids_from_zero_from_first_exp: If True, original class IDs
          will be remapped so that they will appear as having an ascending
          order. For instance, if the resulting class order after shuffling
          (or defined by fixed_class_order) is [23, 34, 11, 7, 6, ...] and
          class_ids_from_zero_from_first_exp is True, then all the patterns
          belonging to class 23 will appear as belonging to class "0",
          class "34" will be mapped to "1", class "11" to "2" and so on.
          This is very useful when drawing confusion matrices and when dealing
          with algorithms with dynamic head expansion. Defaults to False.
          Mutually exclusive with the ``class_ids_from_zero_in_each_exp``
          parameter.
      :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).
          If no transformation is passed, the default train transformation
          will be used.
      :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).
          If no transformation is passed, the default test transformation
          will be used.
      :param dataset_root_cifar10: The root path of the CIFAR-10 dataset.
          Defaults to None, which means that the default location for
          'cifar10' will be used.
      :param dataset_root_cifar100: The root path of the CIFAR-100 dataset.
          Defaults to None, which means that the default location for
          'cifar100' will be used.
  
      :returns: A properly initialized :class:`NCScenario` instance.
      """
  
      cifar10_train, cifar10_test = get_cifar10_dataset(
          dataset_root_cifar10
      )
      cifar100_train, cifar100_test = get_cifar100_dataset(
          dataset_root_cifar100
      )
  
      (
          cifar_10_100_train,
          cifar_10_100_test,
          _,
      ) = _concat_taskaware_classification_datasets_sequentially(
          [cifar10_train, cifar100_train], [cifar10_test, cifar100_test]
      )
      # cifar10 classes
      class_order = [_ for _ in range(10)]
      # if a class order is defined (for cifar100) the given class labels are
      # appended to the class_order list, adding 10 to them (since the classes
      # 0-9 are the classes of cifar10).
      if fixed_class_order is not None:
          class_order.extend([c + 10 for c in fixed_class_order])
      else:
          random.seed(seed)
          # random shuffling of the cifar100 classes (labels 10-109)
          cifar_100_class_order = random.sample(range(10, 110), 100)
          class_order.extend(cifar_100_class_order)
  
      return nc_benchmark(
          cifar_10_100_train,
          cifar_10_100_test,
          n_experiences=n_experiences,
          task_labels=False,
          shuffle=False,
          seed=None,
          fixed_class_order=class_order,
          class_ids_from_zero_from_first_exp=class_ids_from_zero_from_first_exp,
          per_exp_classes={0: 10},
          train_transform=train_transform,
          eval_transform=eval_transform,
      )
  
file_path: avalanche/avalanche/benchmarks/classic/ccifar100.py
imports: import random
  from pathlib import Path
  from typing import Sequence, Optional, Union, Any
  from torchvision import transforms
  from avalanche.benchmarks.classic.classic_benchmarks_utils import check_vision_benchmark
  from avalanche.benchmarks.datasets.external_datasets.cifar import get_cifar100_dataset, get_cifar10_dataset
  from avalanche.benchmarks.utils.classification_dataset import _concat_taskaware_classification_datasets_sequentially
  from avalanche.benchmarks import nc_benchmark, NCScenario
label_desc: Use image node_id 2 for calling a global function with example usage: SplitCIFAR110(n_experiences) and returns: nc_benchmark