def miniimagenet(folder, shots, ways, shuffle=True, test_shots=None,
                 seed=None, **kwargs):
    """Helper function to create a meta-dataset for the Mini-Imagenet dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `miniimagenet` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `MiniImagenet` class.

    See also
    --------
    `datasets.MiniImagenet` : Meta-dataset for the Mini-Imagenet dataset.
    """
    defaults = {
        'transform': Compose([Resize(84), ToTensor()])
    }

    return helper_with_default(MiniImagenet, folder, shots, ways,
                               shuffle=shuffle, test_shots=test_shots,
                               seed=seed, defaults=defaults, **kwargs)
Helper function to create a meta-dataset for the Mini-Imagenet dataset.

    Parameters
    ----------
    folder : string
        Root directory where the dataset folder `miniimagenet` exists.

    shots : int
        Number of (training) examples per class in each task. This corresponds
        to `k` in `k-shot` classification.

    ways : int
        Number of classes per task. This corresponds to `N` in `N-way`
        classification.

    shuffle : bool (default: `True`)
        Shuffle the examples when creating the tasks.

    test_shots : int, optional
        Number of test examples per class in each task. If `None`, then the
        number of test examples is equal to the number of training examples per
        class.

    seed : int, optional
        Random seed to be used in the meta-dataset.

    kwargs
        Additional arguments passed to the `MiniImagenet` class.

    See also
    --------
    `datasets.MiniImagenet` : Meta-dataset for the Mini-Imagenet dataset.
    
torchmeta/datasets/helpers.py