code: def SplitImageNet(
      dataset_root: Union[str, Path],
      *,
      n_experiences=10,
      per_exp_classes=None,
      return_task_id=False,
      seed=0,
      fixed_class_order=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_train_transform,
      eval_transform: Optional[Any] = _default_eval_transform,
  ):
      """
      Creates a CL benchmark using the ImageNet dataset.
  
      If the dataset is not present in the computer, **this method will NOT be
      able 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 dataset_root: Base path where Imagenet data is stored.
      :param n_experiences: The number of experiences in the current benchmark.
      :param per_exp_classes: Is not None, a dictionary whose keys are
          (0-indexed) experience IDs and their values are the number of classes
          to include in the respective experiences. The dictionary doesn't
          have to contain a key for each experience! All the remaining exps
          will contain an equal amount of the remaining classes. The
          remaining number of classes must be divisible without remainder
          by the remaining number of experiences. For instance,
          if you want to include 50 classes in the first experience
          while equally distributing remaining classes across remaining
          experiences, just pass the "{0: 50}" dictionary as the
          per_experience_classes parameter. Defaults to None.
      :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.
  
      :returns: A properly initialized :class:`NCScenario` instance.
      """
  
      train_set, test_set = _get_imagenet_dataset(dataset_root)
  
      return nc_benchmark(
          train_dataset=train_set,
          test_dataset=test_set,
          n_experiences=n_experiences,
          task_labels=return_task_id,
          per_exp_classes=per_exp_classes,
          seed=seed,
          fixed_class_order=fixed_class_order,
          shuffle=shuffle,
          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/cimagenet.py
imports: from pathlib import Path
  from typing import Union, Optional, Any
  from avalanche.benchmarks.classic.classic_benchmarks_utils import check_vision_benchmark
  from avalanche.benchmarks.datasets import ImageNet
  from avalanche.benchmarks import nc_benchmark
  from torchvision import transforms
label_desc: Use image node_id 1 for calling a global function with example usage: SplitImageNet(dataset_root) and returns: nc_benchmark