def PermutedMNIST(
    n_experiences: int,
    *,
    return_task_id=False,
    seed: Optional[int] = None,
    train_transform: Optional[Any] = _default_mnist_train_transform,
    eval_transform: Optional[Any] = _default_mnist_eval_transform,
    dataset_root: Optional[Union[str, Path]] = None
) -> NCScenario:
    """
    Creates a Permuted MNIST benchmark.

    If the dataset is not present in the computer, this method will
    automatically download and store it.

    Random pixel permutations are used to permute the MNIST images in
    ``n_experiences`` different manners. This means that each experience is
    composed of all the original 10 MNIST classes, but the pixel in the images
    are permuted in a different way.

    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.

    A progressive task label, starting from 0, is applied 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 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 n_experiences: The number of experiences (tasks) in the current
        benchmark. It indicates how many different permutations of the MNIST
        dataset have to be created.
        The value of this parameter should be a divisor of 10.
    :param seed: A valid int used to initialize the random number generator.
        Can be None.
    :param train_transform: The transformation to apply to the training data
        before the random permutation, 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
        before the random permutation, 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 'mnist' will be used.

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

    list_train_dataset = []
    list_test_dataset = []
    rng_permute = np.random.RandomState(seed)

    mnist_train, mnist_test = get_mnist_dataset(dataset_root)

    # for every incremental experience
    for _ in range(n_experiences):
        # choose a random permutation of the pixels in the image
        idx_permute = torch.from_numpy(rng_permute.permutation(784)).type(torch.int64)

        permutation = PixelsPermutation(idx_permute)

        # Freeze the permutation
        permuted_train = make_avalanche_dataset(
            _make_taskaware_classification_dataset(mnist_train),
            frozen_transform_groups=DefaultTransformGroups((permutation, None)),
        )

        permuted_test = make_avalanche_dataset(
            _make_taskaware_classification_dataset(mnist_test),
            frozen_transform_groups=DefaultTransformGroups((permutation, None)),
        )

        list_train_dataset.append(permuted_train)
        list_test_dataset.append(permuted_test)

    return nc_benchmark(
        list_train_dataset,
        list_test_dataset,
        n_experiences=len(list_train_dataset),
        task_labels=return_task_id,
        shuffle=False,
        class_ids_from_zero_in_each_exp=True,
        one_dataset_per_exp=True,
        train_transform=train_transform,
        eval_transform=eval_transform,
    )

    Creates a Permuted MNIST benchmark.

    If the dataset is not present in the computer, this method will
    automatically download and store it.

    Random pixel permutations are used to permute the MNIST images in
    ``n_experiences`` different manners. This means that each experience is
    composed of all the original 10 MNIST classes, but the pixel in the images
    are permuted in a different way.

    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.

    A progressive task label, starting from 0, is applied 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 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 n_experiences: The number of experiences (tasks) in the current
        benchmark. It indicates how many different permutations of the MNIST
        dataset have to be created.
        The value of this parameter should be a divisor of 10.
    :param seed: A valid int used to initialize the random number generator.
        Can be None.
    :param train_transform: The transformation to apply to the training data
        before the random permutation, 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
        before the random permutation, 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 'mnist' will be used.

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