class_docstr: (RNN-)Transducer decoder module.
  
  Args:
      vocab_size: Output dimension.
      layers_type: (RNN-)Decoder layers type.
      num_layers: Number of decoder layers.
      hidden_size: Number of decoder units per layer.
      dropout: Dropout rate for decoder layers.
      dropout_embed: Dropout rate for embedding layer.
      embed_pad: Embed/Blank symbol ID.
docstr: Encode source label sequences.
  
  Args:
      labels: Label ID sequences. (B, L)
  
  Returns:
      dec_out: Decoder output sequences. (B, T, U, D_dec)
docstr_tok: ["Encode","source","label","sequences",".","Args",":","labels",":","Label","ID","sequences",".","(","B",",","L",")","Returns",":","dec_out",":","Decoder","output","sequences",".","(","B",",","T",",","U",",","D_dec",")"]
code: def forward(self, labels: torch.Tensor) -> torch.Tensor:
      """Encode source label sequences.
  
      Args:
          labels: Label ID sequences. (B, L)
  
      Returns:
          dec_out: Decoder output sequences. (B, T, U, D_dec)
  
      """
      init_state = self.init_state(labels.size(0))
      dec_embed = self.dropout_embed(self.embed(labels))
  
      dec_out, _ = self.rnn_forward(dec_embed, init_state)
  
      return dec_out
  
file_path: espnet/espnet2/asr/decoder/transducer_decoder.py
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  from typeguard import check_argument_types
  from espnet2.asr.decoder.abs_decoder import AbsDecoder
  from espnet2.asr.transducer.beam_search_transducer import ExtendedHypothesis, Hypothesis
label_desc: Use image node_id 5 for calling the TransducerDecoder obj's underlying member method code with example usage: obj.forward(labels) and returns: dec_out