def METHOD_NAME(self):
    f = np.load(osp.join(self.raw_dir, 'adj_full.npz'))
    adj = sp.csr_matrix((f['data'], f['indices'], f['indptr']), f['shape'])
    adj = adj.tocoo()
    row = torch.from_numpy(adj.row).to(torch.long)
    col = torch.from_numpy(adj.col).to(torch.long)
    edge_index = torch.stack([row, col], dim=0)
    x = np.load(osp.join(self.raw_dir, 'feats.npy'))
    x = torch.from_numpy(x).to(torch.float)
    ys = [-1] * x.size(0)
    with open(osp.join(self.raw_dir, 'class_map.json')) as f:
        class_map = json.load(f)
        for key, item in class_map.items():
            ys[int(key)] = item
    y = torch.tensor(ys)
    with open(osp.join(self.raw_dir, 'role.json')) as f:
        role = json.load(f)
    train_mask = torch.zeros(x.size(0), dtype=torch.bool)
    train_mask[torch.tensor(role['tr'])] = True
    val_mask = torch.zeros(x.size(0), dtype=torch.bool)
    val_mask[torch.tensor(role['va'])] = True
    test_mask = torch.zeros(x.size(0), dtype=torch.bool)
    test_mask[torch.tensor(role['te'])] = True
    data = Data(x=x, edge_index=edge_index, y=y, train_mask=train_mask,
                val_mask=val_mask, test_mask=test_mask)
    data = data if self.pre_transform is None else self.pre_transform(data)
    torch.save(self.collate([data]), self.processed_paths[0])