code: def ctc_decoder(
      lexicon: Optional[str],
      tokens: Union[str, List[str]],
      lm: Union[str, CTCDecoderLM] = None,
      lm_dict: Optional[str] = None,
      nbest: int = 1,
      beam_size: int = 50,
      beam_size_token: Optional[int] = None,
      beam_threshold: float = 50,
      lm_weight: float = 2,
      word_score: float = 0,
      unk_score: float = float("-inf"),
      sil_score: float = 0,
      log_add: bool = False,
      blank_token: str = "-",
      sil_token: str = "|",
      unk_word: str = "<unk>",
  ) -> CTCDecoder:
      """Builds an instance of :class:`CTCDecoder`.
  
      Args:
          lexicon (str or None): lexicon file containing the possible words and corresponding spellings.
              Each line consists of a word and its space separated spelling. If `None`, uses lexicon-free
              decoding.
          tokens (str or List[str]): file or list containing valid tokens. If using a file, the expected
              format is for tokens mapping to the same index to be on the same line
          lm (str, CTCDecoderLM, or None, optional): either a path containing KenLM language model,
              custom language model of type `CTCDecoderLM`, or `None` if not using a language model
          lm_dict (str or None, optional): file consisting of the dictionary used for the LM, with a word
              per line sorted by LM index. If decoding with a lexicon, entries in lm_dict must also occur
              in the lexicon file. If `None`, dictionary for LM is constructed using the lexicon file.
              (Default: None)
          nbest (int, optional): number of best decodings to return (Default: 1)
          beam_size (int, optional): max number of hypos to hold after each decode step (Default: 50)
          beam_size_token (int, optional): max number of tokens to consider at each decode step.
              If `None`, it is set to the total number of tokens (Default: None)
          beam_threshold (float, optional): threshold for pruning hypothesis (Default: 50)
          lm_weight (float, optional): weight of language model (Default: 2)
          word_score (float, optional): word insertion score (Default: 0)
          unk_score (float, optional): unknown word insertion score (Default: -inf)
          sil_score (float, optional): silence insertion score (Default: 0)
          log_add (bool, optional): whether or not to use logadd when merging hypotheses (Default: False)
          blank_token (str, optional): token corresponding to blank (Default: "-")
          sil_token (str, optional): token corresponding to silence (Default: "|")
          unk_word (str, optional): word corresponding to unknown (Default: "<unk>")
  
      Returns:
          CTCDecoder: decoder
  
      Example
          >>> decoder = ctc_decoder(
          >>>     lexicon="lexicon.txt",
          >>>     tokens="tokens.txt",
          >>>     lm="kenlm.bin",
          >>> )
          >>> results = decoder(emissions) # List of shape (B, nbest) of Hypotheses
      """
      if lm_dict is not None and type(lm_dict) is not str:
          raise ValueError("lm_dict must be None or str type.")
  
      tokens_dict = _Dictionary(tokens)
  
      # decoder options
      if lexicon:
          lexicon = _load_words(lexicon)
          decoder_options = _LexiconDecoderOptions(
              beam_size=beam_size,
              beam_size_token=beam_size_token
              or tokens_dict.index_size(),
              beam_threshold=beam_threshold,
              lm_weight=lm_weight,
              word_score=word_score,
              unk_score=unk_score,
              sil_score=sil_score,
              log_add=log_add,
              criterion_type=_CriterionType.CTC,
          )
      else:
          decoder_options = _LexiconFreeDecoderOptions(
              beam_size=beam_size,
              beam_size_token=beam_size_token
              or tokens_dict.index_size(),
              beam_threshold=beam_threshold,
              lm_weight=lm_weight,
              sil_score=sil_score,
              log_add=log_add,
              criterion_type=_CriterionType.CTC,
          )
  
      # construct word dict and language model
      word_dict = _get_word_dict(
          lexicon, lm, lm_dict, tokens_dict, unk_word
      )
  
      if type(lm) == str:
          if _KenLM is None:
              raise RuntimeError(
                  "flashlight-text is installed, but KenLM is not installed. "
                  "Please refer to https://github.com/kpu/kenlm#python-module for how to install it."
              )
          lm = _KenLM(lm, word_dict)
      elif lm is None:
          lm = _ZeroLM()
  
      return CTCDecoder(
          nbest=nbest,
          lexicon=lexicon,
          word_dict=word_dict,
          tokens_dict=tokens_dict,
          lm=lm,
          decoder_options=decoder_options,
          blank_token=blank_token,
          sil_token=sil_token,
          unk_word=unk_word,
      )
  
imports: from __future__ import annotations
  import itertools
  from abc import abstractmethod
  from collections import namedtuple
  from typing import Dict, List, NamedTuple, Optional, Tuple, Union
  import torch
  from flashlight.lib.text.decoder import CriterionType, LexiconDecoder, LexiconDecoderOptions, LexiconFreeDecoder, LexiconFreeDecoderOptions, LM, LMState, SmearingMode, Trie, ZeroLM
  from flashlight.lib.text.dictionary import create_word_dict, Dictionary, load_words
  from torchaudio.utils import download_asset
label_desc: Use image node_id 3 for calling a global function with example usage: ctc_decoder(lexicon, tokens, lm, lm_dict, nbest, beam_size, beam_size_token, beam_threshold, lm_weight, word_score, unk_score, sil_score, log_add, blank_token, sil_token, unk_word) and returns: CTCDecoder