class_docstr: RNN decoder module for Transducer model.
  
  Args:
      odim: Output dimension.
      dtype: Decoder units type.
      dlayers: Number of decoder layers.
      dunits: Number of decoder units per layer..
      embed_dim: Embedding layer dimension.
      dropout_rate: Dropout rate for decoder layers.
      dropout_rate_embed: Dropout rate for embedding layer.
      blank_id: Blank symbol ID.
class_docstr_tok: ["RNN","decoder","module","for","Transducer","model",".","Args",":","odim",":","Output","dimension",".","dtype",":","Decoder","units","type",".","dlayers",":","Number","of","decoder","layers",".","dunits",":","Number","of","decoder","units","per","layer","..","embed_dim",":","Embedding","layer","dimension",".","dropout_rate",":","Dropout","rate","for","decoder","layers",".","dropout_rate_embed",":","Dropout","rate","for","embedding","layer",".","blank_id",":","Blank","symbol","ID","."]
docstr: Create decoder hidden states.
  
  Args:
      states: Decoder hidden states. ((N, B, D_dec), (N, B, D_dec))
      new_states: Decoder hidden states. [N x ((1, D_dec), (1, D_dec))]
  
  Returns:
      states: Decoder hidden states. ((N, B, D_dec), (N, B, D_dec))
docstr_tok: ["Create","decoder","hidden","states",".","Args",":","states",":","Decoder","hidden","states",".","(","(","N",",","B",",","D_dec",")",",","(","N",",","B",",","D_dec",")",")","new_states",":","Decoder","hidden","states",".","[","N","x","(","(","1",",","D_dec",")",",","(","1",",","D_dec",")",")","]","Returns",":","states",":","Decoder","hidden","states",".","(","(","N",",","B",",","D_dec",")",",","(","N",",","B",",","D_dec",")",")"]
code: def create_batch_states(
      self,
      states: Tuple[torch.Tensor, Optional[torch.Tensor]],
      new_states: List[Tuple[torch.Tensor, Optional[torch.Tensor]]],
      check_list: Optional[List] = None,
  ) -> List[Tuple[torch.Tensor, Optional[torch.Tensor]]]:
      """Create decoder hidden states.
  
      Args:
          states: Decoder hidden states. ((N, B, D_dec), (N, B, D_dec))
          new_states: Decoder hidden states. [N x ((1, D_dec), (1, D_dec))]
  
      Returns:
          states: Decoder hidden states. ((N, B, D_dec), (N, B, D_dec))
  
      """
      return (
          torch.cat([s[0] for s in new_states], dim=1),
          torch.cat([s[1] for s in new_states], dim=1)
          if self.dtype == "lstm"
          else None,
      )
  
file_path: espnet/espnet/nets/pytorch_backend/transducer/rnn_decoder.py
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  from espnet.nets.transducer_decoder_interface import ExtendedHypothesis, Hypothesis, TransducerDecoderInterface
label_desc: Use image node_id 9 for calling the RNNDecoder obj's underlying member method code with example usage: obj.create_batch_states(states, new_states, check_list) and returns: torch,  unknown