class_docstr: Decoder interface for Transducer models.
docstr: One-step forward hypotheses.
  
  Args:
      hyps: Hypotheses.
      dec_states: Decoder hidden states.
      cache: Pairs of (dec_out, dec_states) for each label sequence. (key)
      use_lm: Whether to compute label ID sequences for LM.
  
  Returns:
      dec_out: Decoder output sequences.
      dec_states: Decoder hidden states.
      lm_labels: Label ID sequences for LM.
code: def batch_score(
      self,
      hyps: Union[List[Hypothesis], List[ExtendedHypothesis]],
      dec_states: Union[
          Tuple[torch.Tensor, Optional[torch.Tensor]],
          List[Optional[torch.Tensor]],
      ],
      cache: Dict[str, Any],
      use_lm: bool,
  ) -> Tuple[
      torch.Tensor,
      Union[
          Tuple[torch.Tensor, Optional[torch.Tensor]],
          List[Optional[torch.Tensor]],
      ],
      torch.Tensor,
  ]:
      """One-step forward hypotheses.
  
      Args:
          hyps: Hypotheses.
          dec_states: Decoder hidden states.
          cache: Pairs of (dec_out, dec_states) for each label sequence. (key)
          use_lm: Whether to compute label ID sequences for LM.
  
      Returns:
          dec_out: Decoder output sequences.
          dec_states: Decoder hidden states.
          lm_labels: Label ID sequences for LM.
  
      """
      raise NotImplementedError("batch_score(...) is not implemented")
  
file_path: espnet/espnet/nets/transducer_decoder_interface.py
imports: from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
label_desc: Use image node_id 3 for calling the TransducerDecoderInterface obj's underlying member method code with example usage: obj.batch_score(hyps, dec_states, cache, use_lm) without return types