class_docstr: RNN and FC stacks for f0 and loudness.
class_docstr_tok: ["RNN","and","FC","stacks","for","f0","and","loudness","."]
code: def call(self, *inputs, **unused_kwargs):
      # Last input is always carried state for stateless RNN.
      inputs = list(inputs)
      if self.stateless:
          state = inputs.pop()
  
      # Initial processing.
      inputs = [stack(x) for stack, x in zip(self.input_stacks, inputs)]
  
      # Run an RNN over the latents.
      x = tf.concat(inputs, axis=-1)
      if self.stateless:
          x, new_state = self.rnn(x, state)
      else:
          x = self.rnn(x)
      x = tf.concat(inputs + [x], axis=-1)
  
      # Final processing.
      x = self.out_stack(x)
      x = self.dense_out(x)
  
      output_dict = nn.split_to_dict(x, self.output_splits)
      if self.stateless:
          output_dict["state"] = new_state
  
      return output_dict
  
imports: from ddsp import core
  from ddsp.training import nn
  import gin
  import tensorflow
label_desc: Use image node_id 2 for calling the RnnFcDecoder obj's underlying member method code with example usage: obj.call() and returns: output_dict