How to use the torchmeta.datasets.utils.get_asset function in torchmeta

all_sample_ids_file = os.path.join(self.root, 'all_sample_ids.json')
            with open(all_sample_ids_file, 'w') as f:
                json.dump(all_sample_ids, f)

            if os.path.isfile(csv_file):
                os.remove(csv_file)

            print('Done')

        self._process_clinical_matrices()

        # Create label files
        for split in ['train', 'val', 'test']:
            filename = os.path.join(self.root, self.filename_tasks.format(split))
            data = get_asset(self.folder, '{0}.json'.format(split), dtype='json')

            with open(filename, 'w') as f:
                labels = sorted([key.split('|', 1) for key in data])
                json.dump(labels, f)

        # Clean up
        for cancer in self.cancers:
            filename = self.clinical_matrix_filename.format(cancer)
            rawpath = os.path.join(clinical_matrices_folder, '{0}.gz'.format(filename))
            if os.path.isfile(rawpath):
                os.remove(rawpath)

return

        filename = os.path.basename(self.download_url)
        download_url(self.download_url, self.root, filename, self.tgz_md5)

        tgz_filename = os.path.join(self.root, filename)
        with tarfile.open(tgz_filename, 'r') as f:
            f.extractall(self.root)
        image_folder = os.path.join(self.root, self.image_folder)

        for split in ['train', 'val', 'test']:
            filename = os.path.join(self.root, self.filename.format(split))
            if os.path.isfile(filename):
                continue

            labels = get_asset(self.folder, '{0}.json'.format(split))
            labels_filename = os.path.join(self.root, self.filename_labels.format(split))
            with open(labels_filename, 'w') as f:
                json.dump(labels, f)

            with h5py.File(filename, 'w') as f:
                group = f.create_group('datasets')
                dtype = h5py.special_dtype(vlen=np.uint8)
                for i, label in enumerate(tqdm(labels, desc=filename)):
                    images = glob.glob(os.path.join(image_folder, label, '*.jpg'))
                    images.sort()
                    dataset = group.create_dataset(label, (len(images),), dtype=dtype)
                    for i, image in enumerate(images):
                        with open(image, 'rb') as f:
                            array = bytearray(f.read())
                            dataset[i] = np.asarray(array, dtype=np.uint8)

def get_task_id_splits(meta_split):
    return get_asset(TCGA.folder, '{}.json'.format(meta_split), dtype='json')

for _, alphabet, character in characters:
                    filenames = glob.glob(os.path.join(self.root, name,
                        alphabet, character, '*.png'))
                    dataset = group.create_dataset('{0}/{1}'.format(alphabet,
                        character), (len(filenames), 105, 105), dtype='uint8')

                    for i, char_filename in enumerate(filenames):
                        image = Image.open(char_filename, mode='r').convert('L')
                        dataset[i] = ImageOps.invert(image)

                shutil.rmtree(os.path.join(self.root, name))

        for split in ['train', 'val', 'test']:
            filename = os.path.join(self.root, self.filename_labels.format(
                'vinyals_', split))
            data = get_asset(self.folder, '{0}.json'.format(split), dtype='json')

            with open(filename, 'w') as f:
                labels = sorted([('images_{0}'.format(name), alphabet, character)
                    for (name, alphabets) in data.items()
                    for (alphabet, characters) in alphabets.items()
                    for character in characters])
                json.dump(labels, f)

def get_cancers():
    return get_asset(TCGA.folder, 'cancers.json', dtype='json')

def download(self):
        if self._check_integrity():
            return
        super(CIFARFSClassDataset, self).download()

        subfolder = os.path.join(self.root, self.subfolder)
        if not os.path.exists(subfolder):
            os.makedirs(subfolder)

        for split in ['train', 'val', 'test']:
            split_filename_labels = os.path.join(subfolder,
                self.filename_labels.format(split))
            if os.path.isfile(split_filename_labels):
                continue

            data = get_asset(self.folder, self.subfolder,
                '{0}.json'.format(split), dtype='json')
            with open(split_filename_labels, 'w') as f:
                json.dump(data, f)