class_docstr: Build inference pipeline from RNNTBundle.
  
  Args:
      bundle (torchaudio.pipelines.RNNTBundle): Bundle object
      beam_width (int): Beam size of beam search decoder.
docstr: Perform streaming inference
code: def infer(self, segment: torch.Tensor) -> str:
      """Perform streaming inference"""
      features, length = self.feature_extractor(segment)
      self.hypotheses, self.state = self.decoder.infer(
          features,
          length,
          self.beam_width,
          state=self.state,
          hypothesis=self.hypotheses,
      )
      transcript = self.token_processor(
          self.hypotheses[0][0], lstrip=False
      )
      return transcript
  
file_path: audio/examples/tutorials/device_asr.py
imports: import torch
  import torchaudio
label_desc: Use image node_id 2 for calling the Pipeline obj's underlying member method code with example usage: obj.infer(segment) and returns: transcript