code: def run_inference(num_iter=100):
      global state, hypothesis
      chunks = []
      feats = []
      for i, (chunk,) in enumerate(stream_iterator, start=1):
          segment = cacher(chunk[:, 0])
          features, length = feature_extractor(segment)
          hypos, state = decoder.infer(
              features, length, 10, state=state, hypothesis=hypothesis
          )
          hypothesis = hypos
          transcript = token_processor(hypos[0][0], lstrip=False)
          print(transcript, end="\r", flush=True)
  
          chunks.append(chunk)
          feats.append(features)
          if i == num_iter:
              break
  
      # Plot the features
      _plot(feats, num_iter)
      return IPython.display.Audio(
          torch.cat(chunks).T.numpy(), rate=bundle.sample_rate
      )
  
file_path: audio/examples/tutorials/online_asr_tutorial.py
imports: import torch
  import torchaudio
  import IPython
  import matplotlib.pyplot
  from torchaudio.io import StreamReader
label_desc: Use image node_id 2 for calling a global function with example usage: run_inference(num_iter) and returns: IPython