class_docstr: RNN decoder module.
  
  Args:
      vocab_size: Vocabulary size.
      embed_size: Embedding size.
      hidden_size: Hidden size..
      rnn_type: Decoder layers type.
      num_layers: Number of decoder layers.
      dropout_rate: Dropout rate for decoder layers.
      embed_dropout_rate: Dropout rate for embedding layer.
      embed_pad: Embedding padding symbol ID.
docstr: One-step forward hypotheses.
  
  Args:
      hyps: Hypotheses.
  
  Returns:
      out: Decoder output sequences. (B, D_dec)
      states: Decoder hidden states. ((N, B, D_dec), (N, B, D_dec) or None)
docstr_tok: ["One-step","forward","hypotheses",".","Args",":","hyps",":","Hypotheses",".","Returns",":","out",":","Decoder","output","sequences",".","(","B",",","D_dec",")","states",":","Decoder","hidden","states",".","(","(","N",",","B",",","D_dec",")",",","(","N",",","B",",","D_dec",")","or","None",")"]
code: def batch_score(
      self,
      hyps: List[Hypothesis],
  ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, Optional[torch.Tensor]]]:
      """One-step forward hypotheses.
  
      Args:
          hyps: Hypotheses.
  
      Returns:
          out: Decoder output sequences. (B, D_dec)
          states: Decoder hidden states. ((N, B, D_dec), (N, B, D_dec) or None)
  
      """
      labels = torch.tensor(
          [[h.yseq[-1]] for h in hyps],
          dtype=torch.long,
          device=self.device,
      )
      embed = self.embed(labels)
  
      states = self.create_batch_states([h.dec_state for h in hyps])
      out, states = self.rnn_forward(embed, states)
  
      return out.squeeze(1), states
  
file_path: espnet/espnet2/asr_transducer/decoder/rnn_decoder.py
imports: from typing import List, Optional, Tuple
  import torch
  from typeguard import check_argument_types
  from espnet2.asr_transducer.beam_search_transducer import Hypothesis
  from espnet2.asr_transducer.decoder.abs_decoder import AbsDecoder
label_desc: Use image node_id 5 for calling the RNNDecoder obj's underlying member method code with example usage: obj.batch_score(hyps) and returns: out,  states