class_docstr: Cache the end of input data and prepend the next input data with it.
  
  Args:
      segment_length (int): The size of main segment.
          If the incoming segment is shorter, then the segment is padded.
      context_length (int): The size of the context, cached and appended.
code: def __init__(self, segment_length: int, context_length: int):
      self.segment_length = segment_length
      self.context_length = context_length
      self.context = torch.zeros([context_length])
  
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 1 to create a new ContextCacher object  with example: obj = ContextCacher(segment_length, context_length)