def SplitAlphabetOmniglot(
    n_experiences: int,
    *,
    return_task_id=False,
    seed: Optional[int] = None,
    fixed_class_order: Optional[Sequence[int]] = None,
    class_ids_from_zero_from_first_exp: bool = False,
    shuffle: bool = True,
    train_transform: Optional[Any] = _default_omniglot_train_transform,
    eval_transform: Optional[Any] = _default_omniglot_eval_transform,
    dataset_root: Optional[Union[str, Path]] = None
):
    """Class-incremental OMNIGLOT with the alphabet used as target.

    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 alphabets (class-incremental scenario).

    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 10.
    :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 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 shuffle: If true, the class order in the incremental experiences is
        randomly shuffled. Default to True.
    :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 'omniglot' will be used.

    :returns: A properly initialized :class:`NCScenario` instance.
    """

    omniglot_train, omniglot_test = _get_omniglot_dataset(dataset_root)
    return nc_benchmark(
        train_dataset=omniglot_train,
        test_dataset=omniglot_test,
        n_experiences=n_experiences,
        task_labels=return_task_id,
        seed=seed,
        fixed_class_order=fixed_class_order,
        shuffle=shuffle,
        class_ids_from_zero_in_each_exp=False,
        class_ids_from_zero_from_first_exp=class_ids_from_zero_from_first_exp,
        train_transform=train_transform,
        eval_transform=eval_transform,
    )
Class-incremental OMNIGLOT with the alphabet used as target.

    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 alphabets (class-incremental scenario).

    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 10.
    :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 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 shuffle: If true, the class order in the incremental experiences is
        randomly shuffled. Default to True.
    :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 'omniglot' will be used.

    :returns: A properly initialized :class:`NCScenario` instance.
    
avalanche/benchmarks/classic/comniglot.py