class_docstr: Build inference pipeline from RNNTBundle.
  
  Args:
      bundle (torchaudio.pipelines.RNNTBundle): Bundle object
      beam_width (int): Beam size of beam search decoder.
code: def __init__(
      self,
      bundle: torchaudio.pipelines.RNNTBundle,
      beam_width: int = 10,
  ):
      self.bundle = bundle
      self.feature_extractor = bundle.get_streaming_feature_extractor()
      self.decoder = bundle.get_decoder()
      self.token_processor = bundle.get_token_processor()
  
      self.beam_width = beam_width
  
      self.state = None
      self.hypotheses = None
  
file_path: audio/examples/tutorials/device_asr.py
imports: import torch
  import torchaudio
label_desc: Use image node_id 1 to create a new Pipeline object  with example: obj = Pipeline(bundle, beam_width)