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: Sets the decoding type of the framework. Can support either char or subword models.
  
  Args:
      decoding_type: Str corresponding to decoding type. Only supports "char" and "subword".
docstr_tok: ["Sets","the","decoding","type","of","the","framework",".","Can","support","either","char","or","subword","models",".","Args",":","decoding_type",":","Str","corresponding","to","decoding","type",".","Only","supports","``","char","''","and","``","subword","''","."]
code: def set_decoding_type(self, decoding_type: str):
      """
      Sets the decoding type of the framework. Can support either char or subword models.
  
      Args:
          decoding_type: Str corresponding to decoding type. Only supports "char" and "subword".
      """
      decoding_type = decoding_type.lower()
      supported_types = ["char", "subword"]
  
      if decoding_type not in supported_types:
          raise ValueError(
              f"Unsupported decoding type. Supported types = {supported_types}.\n"
              f"Given = {decoding_type}"
          )
  
      self.decoding_type = decoding_type
  
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 5 for calling the AbstractBeamCTCInfer obj's underlying member method code with example usage: obj.set_decoding_type(decoding_type) without return types