class_docstr: An abstract RNNT Decoder framework, which can possibly integrate with GreedyRNNTInfer and BeamRNNTInfer classes.
  Represents the abstract RNNT Prediction/Decoder stateful network, which performs autoregressive decoding
  in order to construct the output sequence.
  
  Args:
      vocab_size: Size of the vocabulary, excluding the RNNT blank token.
      blank_idx: Index of the blank token. Can be 0 or size(vocabulary).
      blank_as_pad: Bool flag, whether to allocate an additional token in the Embedding layer
          of this module in order to treat all RNNT `blank` tokens as pad tokens, thereby letting
          the Embedding layer batch tokens more efficiently.
  
          It is mandatory to use this for certain Beam RNNT Infer methods - such as TSD, ALSD.
          It is also more efficient to use greedy batch decoding with this flag.
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docstr: Similar to the predict() method, instead this method scores a Hypothesis during beam search.
  Hypothesis is a dataclass representing one hypothesis in a Beam Search.
  
  Args:
      hypothesis: Refer to rnnt_utils.Hypothesis.
      cache: Dict which contains a cache to avoid duplicate computations.
  
  Returns:
      Returns a tuple (y, states, lm_token) such that:
      y is a torch.Tensor of shape [1, 1, H] representing the score of the last token in the Hypothesis.
      state is a list of RNN states, each of shape [L, 1, H].
      lm_token is the final integer token of the hypothesis.
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code: def score_hypothesis(
      self, hypothesis: Hypothesis, cache: Dict[Tuple[int], Any]
  ) -> Tuple[torch.Tensor, List[torch.Tensor], torch.Tensor]:
      """
      Similar to the predict() method, instead this method scores a Hypothesis during beam search.
      Hypothesis is a dataclass representing one hypothesis in a Beam Search.
  
      Args:
          hypothesis: Refer to rnnt_utils.Hypothesis.
          cache: Dict which contains a cache to avoid duplicate computations.
  
      Returns:
          Returns a tuple (y, states, lm_token) such that:
          y is a torch.Tensor of shape [1, 1, H] representing the score of the last token in the Hypothesis.
          state is a list of RNN states, each of shape [L, 1, H].
          lm_token is the final integer token of the hypothesis.
      """
      raise NotImplementedError()
  
imports: from abc import ABC, abstractmethod
  from typing import Any, Dict, List, Optional, Tuple
  import torch
  from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis
  from nemo.core import NeuralModule
label_desc: Use image node_id 4 for calling the AbstractRNNTDecoder obj's underlying member method code with example usage: obj.score_hypothesis(hypothesis, cache) without return types