class_docstr: A beam CTC decoder.
  
  Provides a common abstraction for sample level beam decoding.
  
  Args:
      blank_id: int, index of the blank token. Can be 0 or len(vocabulary).
      beam_size: int, size of the beam used in the underlying beam search engine.
class_docstr_tok: ["A","beam","CTC","decoder",".","Provides","a","common","abstraction","for","sample","level","beam","decoding",".","Args",":","blank_id",":","int",",","index","of","the","blank","token",".","Can","be","0","or","len","(","vocabulary",")",".","beam_size",":","int",",","size","of","the","beam","used","in","the","underlying","beam","search","engine","."]
docstr: Set the vocabulary of the decoding framework.
  
  Args:
      vocab: List of str. Each token corresponds to its location in the vocabulary emitted by the model.
          Note that this vocabulary must NOT contain the "BLANK" token.
docstr_tok: ["Set","the","vocabulary","of","the","decoding","framework",".","Args",":","vocab",":","List","of","str",".","Each","token","corresponds","to","its","location","in","the","vocabulary","emitted","by","the","model",".","Note","that","this","vocabulary","must","NOT","contain","the","``","BLANK","''","token","."]
code: def set_vocabulary(self, vocab: List[str]):
      """
      Set the vocabulary of the decoding framework.
  
      Args:
          vocab: List of str. Each token corresponds to its location in the vocabulary emitted by the model.
              Note that this vocabulary must NOT contain the "BLANK" token.
      """
      self.vocab = vocab
      self.vocab_index_map = {v: i for i, v in enumerate(vocab)}
      self.index_vocab_map = {i: v for i, v in enumerate(vocab)}
  
imports: import math
  import os
  from dataclasses import dataclass, field
  from typing import List, Optional, Tuple, Union
  import torch
  from nemo.collections.asr.parts.utils import rnnt_utils
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.core.classes import Typing, typecheck
  from nemo.core.neural_types import HypothesisType, LengthsType, LogprobsType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the AbstractBeamCTCInfer obj's underlying member method code with example usage: obj.set_vocabulary(vocab) without return types