def METHOD_NAME(self):
    raw = np.load(self.raw_paths[0], 'r')
    x = torch.from_numpy(raw['node_features'])
    y = torch.from_numpy(raw['node_labels'])
    edge_index = torch.from_numpy(raw['edges']).t().contiguous()
    edge_index = to_undirected(edge_index, num_nodes=x.size(0))
    train_mask = torch.from_numpy(raw['train_masks']).t().contiguous()
    val_mask = torch.from_numpy(raw['val_masks']).t().contiguous()
    test_mask = torch.from_numpy(raw['test_masks']).t().contiguous()
    data = Data(x=x, y=y, edge_index=edge_index, train_mask=train_mask,
                val_mask=val_mask, test_mask=test_mask)
    if self.pre_transform is not None:
        data = self.pre_transform(data)
    torch.save(self.collate([data]), self.processed_paths[0])