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 __call__(self, chunk: torch.Tensor):
      if chunk.size(0) < self.segment_length:
          chunk = torch.nn.functional.pad(
              chunk, (0, self.segment_length - chunk.size(0))
          )
      chunk_with_context = torch.cat((self.context, chunk))
      self.context = chunk[-self.context_length :]
      return chunk_with_context
  
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 the ContextCacher obj's underlying member method code with example usage: obj.__call__(chunk) and returns: chunk_with_context