def beam_decode_step(inst_dec_beams, len_dec_seq, enc_output,
                             inst_idx_to_position_map, n_bm,
                             memory_key_padding_mask):
            ''' Decode and update beam status, and then return active beam idx '''

            def prepare_beam_dec_seq(inst_dec_beams, len_dec_seq):
                dec_partial_seq = [
                    b.get_current_state() for b in inst_dec_beams if not b.done
                ]
                dec_partial_seq = torch.stack(dec_partial_seq)
                dec_partial_seq = dec_partial_seq.reshape([-1, len_dec_seq])
                return dec_partial_seq

            def predict_word(dec_seq, enc_output, n_active_inst, n_bm,
                             memory_key_padding_mask):
                dec_seq = self.embedding(dec_seq).permute(1, 0, 2)
                dec_seq = self.positional_encoding(dec_seq)
                tgt_mask = self.generate_square_subsequent_mask(
                    dec_seq.shape[0])
                dec_output = self.decoder(
                    dec_seq,
                    enc_output,
                    tgt_mask=tgt_mask,
                    tgt_key_padding_mask=None,
                    memory_key_padding_mask=memory_key_padding_mask, )
                dec_output = dec_output.permute(1, 0, 2)
                dec_output = dec_output[:,
                                        -1, :]  # Pick the last step: (bh * bm) * d_h
                word_prob = F.softmax(self.tgt_word_prj(dec_output), dim=1)
                word_prob = torch.reshape(word_prob, (n_active_inst, n_bm, -1))
                return word_prob

            def collect_active_inst_idx_list(inst_beams, word_prob,
                                             inst_idx_to_position_map):
                active_inst_idx_list = []
                for inst_idx, inst_position in inst_idx_to_position_map.items():
                    is_inst_complete = inst_beams[inst_idx].advance(word_prob[
                        inst_position])
                    if not is_inst_complete:
                        active_inst_idx_list += [inst_idx]

                return active_inst_idx_list

            n_active_inst = len(inst_idx_to_position_map)
            dec_seq = prepare_beam_dec_seq(inst_dec_beams, len_dec_seq)
            word_prob = predict_word(dec_seq, enc_output, n_active_inst, n_bm,
                                     None)
            # Update the beam with predicted word prob information and collect incomplete instances
            active_inst_idx_list = collect_active_inst_idx_list(
                inst_dec_beams, word_prob, inst_idx_to_position_map)
            return active_inst_idx_list
 Decode and update beam status, and then return active beam idx 
pytorchocr/modeling/heads/rec_nrtr_head.py