class_docstr: Use MFCCs as latent variables, compress to single timestep.
code: def __init__(
      self,
      rnn_channels=512,
      rnn_type="gru",
      z_dims=512,
      mean_aggregate=False,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.mean_aggregate = mean_aggregate
  
      # Layers.
      self.norm_in = nn.Normalize("instance")
      self.rnn = nn.Rnn(rnn_channels, rnn_type)
      self.dense_z = tfkl.Dense(z_dims)
  
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 MfccRnnEncoder object from inherited base classes: ZEncoder with example: obj = MfccRnnEncoder(rnn_channels, rnn_type, z_dims, mean_aggregate)