class_docstr: Get latent variable from MFCCs, loudness, and pitch.
code: def __init__(
      self,
      net=None,
      z_dims=128,
      input_keys=("f0_scaled", "ld_scaled"),
      mfcc_bins=60,
      fft_size=1024,
      mel_bins=128,
      pool_time=True,
      **kwargs,
  ):
      self.input_keys = input_keys
      super().__init__(input_keys, **kwargs)
      self.compute_mfccs = "audio" in self.input_keys
      self.mfcc_bins = mfcc_bins
      self.fft_size = fft_size
      self.mel_bins = mel_bins
      self.pool_time = pool_time
  
      # Layers.
      self.net = net
      self.norm = nn.Normalize("layer")
      self.dense_out = tfkl.Dense(z_dims)
      if self.compute_mfccs:
          self.norm_mfcc = 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 ExpressionEncoder object from inherited base classes: ZEncoder with example: obj = ExpressionEncoder(net, z_dims, input_keys, mfcc_bins, fft_size, mel_bins, pool_time)