code: def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor):
      # collect all finished sequences, including patience, and add unfinished ones if not enough
      sum_logprobs = sum_logprobs.cpu()
      for i, sequences in enumerate(self.finished_sequences):
          if (
              len(sequences) < self.beam_size
          ):  # when not enough sequences are finished
              for j in list(np.argsort(sum_logprobs[i]))[::-1]:
                  sequence = preceding_tokens[i, j].tolist() + [
                      self.eot
                  ]
                  sequences[tuple(sequence)] = sum_logprobs[i][j].item()
                  if len(sequences) >= self.beam_size:
                      break
  
      tokens: List[List[Tensor]] = [
          [torch.tensor(seq) for seq in sequences.keys()]
          for sequences in self.finished_sequences
      ]
      sum_logprobs: List[List[float]] = [
          list(sequences.values())
          for sequences in self.finished_sequences
      ]
      return tokens, sum_logprobs
  
imports: from dataclasses import dataclass, field, replace
  from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch.distributions import Categorical
  from .audio import CHUNK_LENGTH
  from .tokenizer import Tokenizer, get_tokenizer
  from .utils import compression_ratio
label_desc: Use image node_id 4 for calling the BeamSearchDecoder obj's underlying member method code with example usage: obj.finalize(preceding_tokens, sum_logprobs) and returns: tokens,  sum_logprobs