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 tokenizer of the decoding framework.
  
  Args:
      tokenizer: NeMo tokenizer object, which inherits from TokenizerSpec.
docstr_tok: ["Set","the","tokenizer","of","the","decoding","framework",".","Args",":","tokenizer",":","NeMo","tokenizer","object",",","which","inherits","from","TokenizerSpec","."]
code: def set_tokenizer(self, tokenizer: TokenizerSpec):
      """
      Set the tokenizer of the decoding framework.
  
      Args:
          tokenizer: NeMo tokenizer object, which inherits from TokenizerSpec.
      """
      self.tokenizer = tokenizer
  
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 6 for calling the AbstractBeamCTCInfer obj's underlying member method code with example usage: obj.set_tokenizer(tokenizer) without return types