docstr: Ensure input output keys are overwritten by constructor.
  
  Args:
    layer_class: Which type of class definition to use for test_layer.
docstr_tok: ["Ensure","input","output","keys","are","overwritten","by","constructor",".","Args",":","layer_class",":","Which","type","of","class","definition","to","use","for","test_layer","."]
code: def test_renaming_input_output_keys(self, layer_class):
      """Ensure input output keys are overwritten by constructor.
  
      Args:
        layer_class: Which type of class definition to use for test_layer.
      """
      input_keys = ["input_1", "input_2"]
      output_keys = ["output_1", "output_2", "output_3"]
      test_layer = getattr(self, layer_class)(
          input_keys=input_keys, output_keys=output_keys
      )
      # Arg inputs.
      outputs = test_layer(self.x, self.x)
      self.assert_output_shapes_are_correct(test_layer, outputs)
  
      # Dict inputs.
      outputs = test_layer({"input_1": self.x, "input_2": self.x})
      self.assert_output_shapes_are_correct(test_layer, outputs)
  
      # Make sure original input_keys no longer are correct.
      with self.assertRaises(TypeError):
          test_layer({"x1": self.x, "x2": self.x})
  
imports: from absl.testing import parameterized
  from ddsp.training import nn
  import numpy
  import tensorflow.compat.v2
label_desc: Use image node_id 5 for calling the DictLayerTest obj's underlying member method code with example usage: obj.test_renaming_input_output_keys(layer_class) without return types