def advance(self, word_probs, attn_out):
        """
        Given prob over words for every last beam `wordLk` and attention
        `attn_out`: Compute and update the beam search.

        Parameters:

        * `word_probs`- probs of advancing from the last step (K x words)
        * `attn_out`- attention at the last step

        Returns: True if beam search is complete.
        """
        num_words = word_probs.size(1)
        if self.stepwise_penalty:
            self.global_scorer.update_score(self, attn_out)
        # force the output to be longer than self.min_length
        cur_len = len(self.next_ys)
        if cur_len < self.min_length:
            for k in range(len(word_probs)):
                word_probs[k][self._eos] = -1e20
        # Sum the previous scores.
        if len(self.prev_ks) > 0:
            beam_scores = word_probs + self.scores.unsqueeze(1).expand_as(word_probs)
            # Don't let EOS have children.
            for i in range(self.next_ys[-1].size(0)):
                if self.next_ys[-1][i] == self._eos:
                    beam_scores[i] = -1e20

            # Block ngram repeats
            if self.block_ngram_repeat > 0:
                ngrams = []
                le = len(self.next_ys)
                for j in range(self.next_ys[-1].size(0)):
                    hyp, _ = self.get_hyp(le - 1, j)
                    ngrams = set()
                    fail = False
                    gram = []
                    for i in range(le - 1):
                        # Last n tokens, n = block_ngram_repeat
                        gram = (gram + [hyp[i].item()])[-self.block_ngram_repeat :]
                        # Skip the blocking if it is in the exclusion list
                        if set(gram) & self.exclusion_tokens:
                            continue
                        if tuple(gram) in ngrams:
                            fail = True
                        ngrams.add(tuple(gram))
                    if fail:
                        beam_scores[j] = -10e20
        else:
            beam_scores = word_probs[0]
        flat_beam_scores = beam_scores.view(-1)
        best_scores, best_scores_id = flat_beam_scores.topk(self.size, 0, True, True)

        self.all_scores.append(self.scores)
        self.scores = best_scores

        # best_scores_id is flattened beam x word array, so calculate which
        # word and beam each score came from
        prev_k = best_scores_id / num_words
        self.prev_ks.append(prev_k)
        self.next_ys.append((best_scores_id - prev_k * num_words))
        self.attn.append(attn_out.index_select(0, prev_k))
        self.global_scorer.update_global_state(self)

        for i in range(self.next_ys[-1].size(0)):
            if self.next_ys[-1][i] == self._eos:
                global_scores = self.global_scorer.score(self, self.scores)
                s = global_scores[i]
                self.finished.append((s, len(self.next_ys) - 1, i))

        # End condition is when top-of-beam is EOS and no global score.
        if self.next_ys[-1][0] == self._eos:
            self.all_scores.append(self.scores)
            self.eos_top = True

        Given prob over words for every last beam `wordLk` and attention
        `attn_out`: Compute and update the beam search.

        Parameters:

        * `word_probs`- probs of advancing from the last step (K x words)
        * `attn_out`- attention at the last step

        Returns: True if beam search is complete.
        
utils_nlp/models/transformers/bertsum/beam.py