class_docstr: torchaudio.models.RNNT()
  
  Recurrent neural network transducer (RNN-T) model.
  
  Note:
      To build the model, please use one of the factory functions.
  
  See Also:
      :class:`torchaudio.pipelines.RNNTBundle`: ASR pipeline with pre-trained models.
  
  Args:
      transcriber (torch.nn.Module): transcription network.
      predictor (torch.nn.Module): prediction network.
      joiner (torch.nn.Module): joint network.
class_docstr_tok: ["torchaudio.models.RNNT","(",")","Recurrent","neural","network","transducer","(","RNN-T",")","model",".","Note",":","To","build","the","model",",","please","use","one","of","the","factory","functions",".","See","Also",":",":","class",":","`","torchaudio.pipelines.RNNTBundle","`",":","ASR","pipeline","with","pre-trained","models",".","Args",":","transcriber","(","torch.nn.Module",")",":","transcription","network",".","predictor","(","torch.nn.Module",")",":","prediction","network",".","joiner","(","torch.nn.Module",")",":","joint","network","."]
code: def __init__(
      self,
      transcriber: _Transcriber,
      predictor: _Predictor,
      joiner: _Joiner,
  ) -> None:
      super().__init__()
      self.transcriber = transcriber
      self.predictor = predictor
      self.joiner = joiner
  
imports: from abc import ABC, abstractmethod
  from typing import List, Optional, Tuple
  import torch
  from torchaudio.models import Emformer
label_desc: Use image node_id 1 to create a new RNNT object from inherited base classes: torch.nn with example: obj = RNNT(transcriber, predictor, joiner)