code: def beam_search(
      x: torch.Tensor,
      sos: int,
      eos: int,
      beam_size: int,
      vocab_size: int,
      scorers: Dict[str, ScorerInterface],
      weights: Dict[str, float],
      token_list: List[str] = None,
      maxlenratio: float = 0.0,
      minlenratio: float = 0.0,
      pre_beam_ratio: float = 1.5,
      pre_beam_score_key: str = "full",
  ) -> list:
      """Perform beam search with scorers.
  
      Args:
          x (torch.Tensor): Encoded speech feature (T, D)
          sos (int): Start of sequence id
          eos (int): End of sequence id
          beam_size (int): The number of hypotheses kept during search
          vocab_size (int): The number of vocabulary
          scorers (dict[str, ScorerInterface]): Dict of decoder modules
              e.g., Decoder, CTCPrefixScorer, LM
              The scorer will be ignored if it is `None`
          weights (dict[str, float]): Dict of weights for each scorers
              The scorer will be ignored if its weight is 0
          token_list (list[str]): List of tokens for debug log
          maxlenratio (float): Input length ratio to obtain max output length.
              If maxlenratio=0.0 (default), it uses a end-detect function
              to automatically find maximum hypothesis lengths
          minlenratio (float): Input length ratio to obtain min output length.
          pre_beam_score_key (str): key of scores to perform pre-beam search
          pre_beam_ratio (float): beam size in the pre-beam search
              will be `int(pre_beam_ratio * beam_size)`
  
      Returns:
          list: N-best decoding results
  
      """
      ret = BeamSearch(
          scorers,
          weights,
          beam_size=beam_size,
          vocab_size=vocab_size,
          pre_beam_ratio=pre_beam_ratio,
          pre_beam_score_key=pre_beam_score_key,
          sos=sos,
          eos=eos,
          token_list=token_list,
      ).forward(x=x, maxlenratio=maxlenratio, minlenratio=minlenratio)
      return [h.asdict() for h in ret]
  
imports: import logging
  from itertools import chain
  from typing import Any, Dict, List, NamedTuple, Tuple, Union
  import torch
  from espnet.nets.e2e_asr_common import end_detect
  from espnet.nets.scorer_interface import PartialScorerInterface, ScorerInterface
label_desc: Use image node_id 1 for calling a global function with example usage: beam_search(x, sos, eos, beam_size, vocab_size, scorers, weights, token_list, maxlenratio, minlenratio, pre_beam_ratio, pre_beam_score_key) and returns: unknown