class_docstr: Compute mfcc features of every frame in speech, return a float tensor
  with size (num_channels, num_frames, num_frequencies).
code: def __init__(self, config: dict):
      super().__init__(config)
      self.framepow = Framepow(config)
      self.fbank = Fbank(config)
  
file_path: delta/delta/data/frontend/mfcc.py
imports: import delta.compat
  from core.ops import py_x_ops
  from delta.utils.hparam import HParams
  from delta.data.frontend.base_frontend import BaseFrontend
  from delta.data.frontend.fbank import Fbank
  from delta.data.frontend.framepow import Framepow
  import copy
label_desc: Use image node_id 1 to create a new Mfcc object from inherited base classes: BaseFrontend with example: obj = Mfcc(config)