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: Used for batched beam search algorithms. Similar to score_hypothesis method.
  
  Args:
      hypothesis: List of Hypotheses. Refer to rnnt_utils.Hypothesis.
      cache: Dict which contains a cache to avoid duplicate computations.
      batch_states: List of torch.Tensor which represent the states of the RNN for this batch.
          Each state is of shape [L, B, H]
  
  Returns:
      Returns a tuple (b_y, b_states, lm_tokens) such that:
      b_y is a torch.Tensor of shape [B, 1, H] representing the scores of the last tokens in the Hypotheses.
      b_state is a list of list of RNN states, each of shape [L, B, H].
          Represented as B x List[states].
      lm_token is a list of the final integer tokens of the hypotheses in the batch.
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code: def batch_score_hypothesis(
      self,
      hypotheses: List[Hypothesis],
      cache: Dict[Tuple[int], Any],
      batch_states: List[torch.Tensor],
  ) -> Tuple[torch.Tensor, List[torch.Tensor], torch.Tensor]:
      """
      Used for batched beam search algorithms. Similar to score_hypothesis method.
  
      Args:
          hypothesis: List of Hypotheses. Refer to rnnt_utils.Hypothesis.
          cache: Dict which contains a cache to avoid duplicate computations.
          batch_states: List of torch.Tensor which represent the states of the RNN for this batch.
              Each state is of shape [L, B, H]
  
      Returns:
          Returns a tuple (b_y, b_states, lm_tokens) such that:
          b_y is a torch.Tensor of shape [B, 1, H] representing the scores of the last tokens in the Hypotheses.
          b_state is a list of list of RNN states, each of shape [L, B, H].
              Represented as B x List[states].
          lm_token is a list of the final integer tokens of the hypotheses in the batch.
      """
      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 5 for calling the AbstractRNNTDecoder obj's underlying member method code with example usage: obj.batch_score_hypothesis(hypotheses, cache, batch_states) without return types