class_docstr: Use MFCCs as latent variables.
code: def __init__(
      self,
      fft_sizes=(1024,),
      mel_bins=(128,),
      mfcc_bins=(30,),
      time_steps=250,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.fft_sizes = ddsp.core.make_iterable(fft_sizes)
      self.mel_bins = ddsp.core.make_iterable(mel_bins)
      self.mfcc_bins = ddsp.core.make_iterable(mfcc_bins)
      self.time_steps = time_steps
  
      # Layers.
      self.norm_out = nn.Normalize("instance")
  
imports: import ddsp
  from ddsp import spectral_ops
  from ddsp.training import nn
  import gin
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new MfccEncoder object from inherited base classes: ZEncoder with example: obj = MfccEncoder(fft_sizes, mel_bins, mfcc_bins, time_steps)