class_docstr: WaveNet style 1-D dilated convolution with optional conditioning.
class_docstr_tok: ["WaveNet","style","1-D","dilated","convolution","with","optional","conditioning","."]
docstr: Split x and z inputs and run preconditioning.
docstr_tok: ["Split","x","and","z","inputs","and","run","preconditioning","."]
code: def _parse_inputs(self, inputs):
      """Split x and z inputs and run preconditioning."""
      if self.conditional:
          x = tf.concat(inputs[: -self.n_conditioning], axis=-1)
          z = tf.concat(inputs[-self.n_conditioning :], axis=-1)
          if self.precondition_stack is not None:
              z = self.precondition_stack(z)
          return [x, z]
      else:
          return tf.concat(inputs, axis=-1)
  
imports: from ddsp import core
  from ddsp.training import nn
  import gin
  import tensorflow
label_desc: Use image node_id 2 for calling the DilatedConvDecoder obj's underlying member method code with example usage: obj._parse_inputs(inputs) and returns: list, tf