code: def SplitCIFAR100(
      n_experiences: int,
      *,
      first_exp_with_half_classes: bool = False,
      return_task_id=False,
      seed: Optional[int] = None,
      fixed_class_order: Optional[Sequence[int]] = None,
      shuffle: bool = True,
      class_ids_from_zero_in_each_exp: bool = False,
      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: Optional[Union[str, Path]] = None,
  ):
      """
      Creates a CL benchmark using the CIFAR100 dataset.
  
      If the dataset is not present in the computer, this method will
      automatically download and store it.
  
      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 doesn't force a choice on the availability of task labels,
      a choice that is left to the user (see the `return_task_id` parameter for
      more info on task labels).
  
      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 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 incremental experiences in the current
          benchmark. The value of this parameter should be a divisor of 100 if
          first_task_with_half_classes is False, a divisor of 50 otherwise.
      :param first_exp_with_half_classes: A boolean value that indicates if a
          first pretraining batch containing half of the classes should be used.
          If it's True, a pretraining experience with half of the classes (50 for
          cifar100) is used. If this parameter is False no pretraining task
          will be used, and the dataset is simply split into a the number of
          experiences defined by the parameter n_experiences. Default to False.
      :param return_task_id: if True, a progressive task id is returned for every
          experience. If False, all experiences will have a task ID of 0.
      :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. If None, value of ``seed`` will be used to define the class
          order. If non-None, ``seed`` parameter will be ignored.
          Defaults to None.
      :param shuffle: If true, the class order in the incremental experiences is
          randomly shuffled. Default to True.
      :param class_ids_from_zero_in_each_exp: If True, original class IDs
          will be mapped to range [0, n_classes_in_exp) for each experience.
          Defaults to False. Mutually exclusive with the
          ``class_ids_from_zero_from_first_exp`` parameter.
      :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: The root path of the dataset. Defaults to None, which
          means that the default location for 'cifar100' will be used.
  
      :returns: A properly initialized :class:`NCScenario` instance.
      """
      cifar_train, cifar_test = get_cifar100_dataset(dataset_root)
  
      return nc_benchmark(
          train_dataset=cifar_train,
          test_dataset=cifar_test,
          n_experiences=n_experiences,
          task_labels=return_task_id,
          seed=seed,
          fixed_class_order=fixed_class_order,
          shuffle=shuffle,
          per_exp_classes={0: 50}
          if first_exp_with_half_classes
          else None,
          class_ids_from_zero_in_each_exp=class_ids_from_zero_in_each_exp,
          class_ids_from_zero_from_first_exp=class_ids_from_zero_from_first_exp,
          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 1 for calling a global function with example usage: SplitCIFAR100(n_experiences) and returns: nc_benchmark