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: Copy states from new state to old state at certain indices.
  
  Args:
      old_states(list): packed decoder states
          (L x B x H, L x B x H)
  
      new_states: packed decoder states
          (L x B x H, L x B x H)
  
      ids (list): List of indices to copy states at.
  
      value (optional float): If a value should be copied instead of a state slice, a float should be provided
  
  Returns:
      batch of decoder states with partial copy at ids (or a specific value).
          (L x B x H, L x B x H)
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code: def batch_copy_states(
      self,
      old_states: List[torch.Tensor],
      new_states: List[torch.Tensor],
      ids: List[int],
      value: Optional[float] = None,
  ) -> List[torch.Tensor]:
      """Copy states from new state to old state at certain indices.
  
      Args:
          old_states(list): packed decoder states
              (L x B x H, L x B x H)
  
          new_states: packed decoder states
              (L x B x H, L x B x H)
  
          ids (list): List of indices to copy states at.
  
          value (optional float): If a value should be copied instead of a state slice, a float should be provided
  
      Returns:
          batch of decoder states with partial copy at ids (or a specific value).
              (L x B x H, L x B x H)
      """
      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 9 for calling the AbstractRNNTDecoder obj's underlying member method code with example usage: obj.batch_copy_states(old_states, new_states, ids, value) without return types