gistfile1.txt

    def _process_tensors(self, data):
        # Truncate it to padding len
        article_seq = [d.article_seq[:self.enc_max_len] for d in data]
        abstract_seq = [d.abstract_seq[:self.dec_max_len - 2] for d in data]
        # -2 is for [START] and [STOP]

        # Add [START] and [STOP] to the target abstract
        for s in abstract_seq:
            s.insert(0, START_TOKEN_ID)
            s.append(STOP_TOKEN_ID)

        # Pad 
        article_seq = [s + [PAD_TOKEN_ID] * (self.enc_max_len - len(s)) 
                            for s in article_seq]
        abstract_seq = [s + [PAD_TOKEN_ID] * (self.dec_max_len - len(s)) 
                            for s in abstract_seq]

        return torch.tensor(article_seq, device=DEVICE).view(-1, len(data)), \
               torch.tensor(abstract_seq, device=DEVICE).view(-1, len(data))

using
enc_max_length = max([len(seq) for seq in article_seq])
dec_max_length = max([len(seq) for seq in abstract_seq])
instead of self.enc_max_len and self.dec_max_len might be a better option :)