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: Encode source label sequences.
  
  Args:
      labels: Label ID sequences. (B, L)
      states: Decoder hidden states. None
  
  Returns:
      embed: Decoder output sequences. (B, U, D_emb)
docstr_tok: ["Encode","source","label","sequences",".","Args",":","labels",":","Label","ID","sequences",".","(","B",",","L",")","states",":","Decoder","hidden","states",".","None","Returns",":","embed",":","Decoder","output","sequences",".","(","B",",","U",",","D_emb",")"]
code: def forward(
      self,
      labels: torch.Tensor,
      states: Optional[Any] = None,
  ) -> torch.Tensor:
      """Encode source label sequences.
  
      Args:
          labels: Label ID sequences. (B, L)
          states: Decoder hidden states. None
  
      Returns:
          embed: Decoder output sequences. (B, U, D_emb)
  
      """
      embed = self.embed_dropout_rate(self.embed(labels))
  
      return embed
  
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 2 for calling the StatelessDecoder obj's underlying member method code with example usage: obj.forward(labels, states) and returns: embed