def METHOD_NAME(self) -> List[Data]:
    with open(self.raw_paths[0], 'rb') as f:
        adjs = pickle.load(f)
    ys = torch.load(self.raw_paths[1]).tolist()
    data_list = []
    for adj, y in zip(adjs, ys):
        row, col = torch.from_numpy(adj.row), torch.from_numpy(adj.col)
        edge_index = torch.stack([row, col], dim=0).to(torch.long)
        edge_index, _ = remove_self_loops(edge_index)
        data = Data(edge_index=edge_index, y=y, num_nodes=adj.shape[0])
        if self.pre_filter is not None and not self.pre_filter(data):
            continue
        if self.pre_transform is not None:
            data = self.pre_transform(data)
        data_list.append(data)
    return data_list