def METHOD_NAME(
    self,
    triple_path: str,
    feature_path: str,
    embeddings: Dict[str, Tensor],
) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
    g1 = read_txt_array(triple_path, sep='\t', dtype=torch.long)
    subj, rel, obj = g1.t()
    x_dict = {}
    with open(feature_path, 'r') as f:
        for line in f:
            info = line.strip().split('\t')
            info = info if len(info) == 2 else info + ['**UNK**']
            seq = info[1].lower().split()
            hs = [embeddings.get(w, embeddings['**UNK**']) for w in seq]
            x_dict[int(info[0])] = torch.stack(hs, dim=0)
    idx = torch.tensor(list(x_dict.keys()))
    assoc = torch.full((idx.max().item() + 1, ), -1, dtype=torch.long)
    assoc[idx] = torch.arange(idx.size(0))
    subj, obj = assoc[subj], assoc[obj]
    edge_index = torch.stack([subj, obj], dim=0)
    edge_index, rel = sort_edge_index(edge_index, rel)
    xs = [None for _ in range(idx.size(0))]
    for i in x_dict.keys():
        xs[assoc[i]] = x_dict[i]
    x = torch.nn.utils.rnn.pad_sequence(xs, batch_first=True)
    return x, edge_index, rel, assoc