How to use the torchmeta.utils.data.ClassDataset function in torchmeta

A Closer Look at Few-shot Classification. International Conference on
           Learning Representations (https://openreview.net/forum?id=HkxLXnAcFQ)

    """
    def __init__(self, root, num_classes_per_task=None, meta_train=False,
                 meta_val=False, meta_test=False, meta_split=None,
                 transform=None, target_transform=None, dataset_transform=None,
                 class_augmentations=None, download=False):
        dataset = CUBClassDataset(root, meta_train=meta_train, meta_val=meta_val,
            meta_test=meta_test, meta_split=meta_split, transform=transform,
            class_augmentations=class_augmentations, download=download)
        super(CUB, self).__init__(dataset, num_classes_per_task,
            target_transform=target_transform, dataset_transform=dataset_transform)


class CUBClassDataset(ClassDataset):
    folder = 'cub'
    download_url = 'http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz'
    tgz_md5 = '97eceeb196236b17998738112f37df78'
    image_folder = 'CUB_200_2011/images'

    filename = '{0}_data.hdf5'
    filename_labels = '{0}_labels.json'

    def __init__(self, root, meta_train=False, meta_val=False, meta_test=False,
                 meta_split=None, transform=None, class_augmentations=None,
                 download=False):
        super(CUBClassDataset, self).__init__(meta_train=meta_train,
            meta_val=meta_val, meta_test=meta_test, meta_split=meta_split,
            class_augmentations=class_augmentations)

        self.root = os.path.join(os.path.expanduser(root), self.folder)

.. [2] Ravi, S. and Larochelle, H. (2016). Optimization as a Model for 
           Few-Shot Learning. (https://openreview.net/forum?id=rJY0-Kcll)
    """
    def __init__(self, root, num_classes_per_task=None, meta_train=False,
                 meta_val=False, meta_test=False, meta_split=None,
                 transform=None, target_transform=None, dataset_transform=None,
                 class_augmentations=None, download=False):
        dataset = MiniImagenetClassDataset(root, meta_train=meta_train,
            meta_val=meta_val, meta_test=meta_test, meta_split=meta_split,
            transform=transform, class_augmentations=class_augmentations,
            download=download)
        super(MiniImagenet, self).__init__(dataset, num_classes_per_task,
            target_transform=target_transform, dataset_transform=dataset_transform)


class MiniImagenetClassDataset(ClassDataset):
    folder = 'miniimagenet'
    # Google Drive ID from https://github.com/renmengye/few-shot-ssl-public
    gdrive_id = '16V_ZlkW4SsnNDtnGmaBRq2OoPmUOc5mY'
    gz_filename = 'mini-imagenet.tar.gz'
    gz_md5 = 'b38f1eb4251fb9459ecc8e7febf9b2eb'
    pkl_filename = 'mini-imagenet-cache-{0}.pkl'

    filename = '{0}_data.hdf5'
    filename_labels = '{0}_labels.json'

    def __init__(self, root, meta_train=False, meta_val=False, meta_test=False,
                 meta_split=None, transform=None, class_augmentations=None,
                 download=False):
        super(MiniImagenetClassDataset, self).__init__(meta_train=meta_train,
            meta_val=meta_val, meta_test=meta_test, meta_split=meta_split,
            class_augmentations=class_augmentations)

import numpy as np
import os
import json
import h5py
from PIL import Image

from torchvision.datasets.utils import check_integrity, download_url
from torchmeta.utils.data import Dataset, ClassDataset


class CIFAR100ClassDataset(ClassDataset):
    folder = 'cifar100'
    subfolder = None
    download_url = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'
    gz_folder = 'cifar-100-python'
    gz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85'
    files_md5 = {
        'train': '16019d7e3df5f24257cddd939b257f8d',
        'test': 'f0ef6b0ae62326f3e7ffdfab6717acfc',
        'meta': '7973b15100ade9c7d40fb424638fde48'
    }

    filename = 'data.hdf5'
    filename_labels = '{0}_labels.json'
    filename_fine_names = 'fine_names.json'

    def __init__(self, root, meta_train=False, meta_val=False, meta_test=False,

for semi-supervised few-shot classification. International Conference 
           on Learning Representations. (https://arxiv.org/abs/1803.00676)
    """
    def __init__(self, root, num_classes_per_task=None, meta_train=False,
                 meta_val=False, meta_test=False, meta_split=None,
                 transform=None, target_transform=None, dataset_transform=None,
                 class_augmentations=None, download=False):
        dataset = TieredImagenetClassDataset(root, meta_train=meta_train,
            meta_val=meta_val, meta_test=meta_test, meta_split=meta_split,
            transform=transform, class_augmentations=class_augmentations,
            download=download)
        super(TieredImagenet, self).__init__(dataset, num_classes_per_task,
            target_transform=target_transform, dataset_transform=dataset_transform)


class TieredImagenetClassDataset(ClassDataset):
    folder = 'tieredimagenet'
    # Google Drive ID from https://github.com/renmengye/few-shot-ssl-public
    gdrive_id = '1g1aIDy2Ar_MViF2gDXFYDBTR-HYecV07'
    tar_filename = 'tiered-imagenet.tar'
    tar_md5 = 'e07e811b9f29362d159a9edd0d838c62'
    tar_folder = 'tiered-imagenet'

    filename = '{0}_data.hdf5'
    filename_labels = '{0}_labels.json'

    def __init__(self, root, meta_train=False, meta_val=False, meta_test=False,
                 meta_split=None, transform=None, class_augmentations=None,
                 download=False):
        super(TieredImagenetClassDataset, self).__init__(meta_train=meta_train,
            meta_val=meta_val, meta_test=meta_test, meta_split=meta_split,
            class_augmentations=class_augmentations)

Information Processing Systems (pp. 3630-3638) (https://arxiv.org/abs/1606.04080)
    """
    def __init__(self, root, num_classes_per_task=None, meta_train=False,
                 meta_val=False, meta_test=False, meta_split=None,
                 use_vinyals_split=True, transform=None, target_transform=None,
                 dataset_transform=None, class_augmentations=None, download=False):
        dataset = OmniglotClassDataset(root, meta_train=meta_train,
            meta_val=meta_val, meta_test=meta_test,
            use_vinyals_split=use_vinyals_split, transform=transform,
            meta_split=meta_split, class_augmentations=class_augmentations,
            download=download)
        super(Omniglot, self).__init__(dataset, num_classes_per_task,
            target_transform=target_transform, dataset_transform=dataset_transform)


class OmniglotClassDataset(ClassDataset):
    folder = 'omniglot'
    download_url_prefix = 'https://github.com/brendenlake/omniglot/raw/master/python'
    zips_md5 = {
        'images_background': '68d2efa1b9178cc56df9314c21c6e718',
        'images_evaluation': '6b91aef0f799c5bb55b94e3f2daec811'
    }

    filename = 'data.hdf5'
    filename_labels = '{0}{1}_labels.json'

    def __init__(self, root, meta_train=False, meta_val=False, meta_test=False,
                 meta_split=None, use_vinyals_split=True, transform=None,
                 class_augmentations=None, download=False):
        super(OmniglotClassDataset, self).__init__(meta_train=meta_train,
            meta_val=meta_val, meta_test=meta_test, meta_split=meta_split,
            class_augmentations=class_augmentations)