docstr: Create some dummy input data for the chain.
docstr_tok: ["Create","some","dummy","input","data","for","the","chain","."]
code: def setUp(self):
      """Create some dummy input data for the chain."""
      super().setUp()
      # Create inputs.
      self.n_batch = 4
      self.x_dims = 5
      self.z_dims = 2
      self.x = tf.ones([self.n_batch, self.x_dims])
      self.inputs = {"test_data": self.x}
      self.gin_config_kwarg_modules = f"""
      import ddsp
  
      ### Modules
      ConfigurableDAGLayer.dag = [
          ('encoder', ['inputs/test_data'], ['z']),
          ('bottleneck', ['encoder/z'], ['z_bottleneck']),
          ('decoder', ['bottleneck/z_bottleneck'], ['reconstruction']),
      ]
      ConfigurableDAGLayer.encoder = @encoder/layers.Dense()
      encoder/layers.Dense.units = {self.x_dims}
  
      ConfigurableDAGLayer.bottleneck = @bottleneck/layers.Dense()
      bottleneck/layers.Dense.units = {self.z_dims}
  
      ConfigurableDAGLayer.decoder = @decoder/layers.Dense()
      decoder/layers.Dense.units = {self.x_dims}
      """
      self.gin_config_dag_modules = f"""
      import ddsp
  
      ### Modules
      ConfigurableDAGLayer.dag = [
          (@encoder/layers.Dense(), ['inputs/test_data'], ['z']),
          (@bottleneck/layers.Dense(), ['encoder/z'], ['z_bottleneck']),
          (@decoder/layers.Dense(), ['bottleneck/z_bottleneck'], ['reconstruction']),
      ]
      encoder/layers.Dense.name = 'encoder'
      encoder/layers.Dense.units = {self.x_dims}
  
      bottleneck/layers.Dense.name = 'bottleneck'
      bottleneck/layers.Dense.units = {self.z_dims}
  
      decoder/layers.Dense.name = 'decoder'
      decoder/layers.Dense.units = {self.x_dims}
      """
  
file_path: ddsp/ddsp/dags_test.py
imports: from absl.testing import parameterized
  from ddsp import dags
  import gin
  import tensorflow
label_desc: Use image node_id 1 for calling the DAGLayerTest obj's underlying member method code with example usage: obj.setUp() without return types