class_docstr: Stateless Transducer decoder module.
  
  Args:
      vocab_size: Output size.
      embed_size: Embedding size.
      embed_dropout_rate: Dropout rate for embedding layer.
      embed_pad: Embed/Blank symbol ID.
class_docstr_tok: ["Stateless","Transducer","decoder","module",".","Args",":","vocab_size",":","Output","size",".","embed_size",":","Embedding","size",".","embed_dropout_rate",":","Dropout","rate","for","embedding","layer",".","embed_pad",":","Embed\/Blank","symbol","ID","."]
docstr: One-step forward hypotheses.
  
  Args:
      hyps: Hypotheses.
  
  Returns:
      out: Decoder output sequences. (B, D_dec)
      states: Decoder hidden states. None
docstr_tok: ["One-step","forward","hypotheses",".","Args",":","hyps",":","Hypotheses",".","Returns",":","out",":","Decoder","output","sequences",".","(","B",",","D_dec",")","states",":","Decoder","hidden","states",".","None"]
code: def batch_score(
      self, hyps: List[Hypothesis]
  ) -> Tuple[torch.Tensor, None]:
      """One-step forward hypotheses.
  
      Args:
          hyps: Hypotheses.
  
      Returns:
          out: Decoder output sequences. (B, D_dec)
          states: Decoder hidden states. None
  
      """
      labels = torch.tensor(
          [[h.yseq[-1]] for h in hyps],
          dtype=torch.long,
          device=self.device,
      )
      embed = self.embed(labels)
  
      return embed.squeeze(1), None
  
file_path: espnet/espnet2/asr_transducer/decoder/stateless_decoder.py
imports: from typing import Any, 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 4 for calling the StatelessDecoder obj's underlying member method code with example usage: obj.batch_score(hyps) and returns: embed,  None