code: def DISABLED_test_nested_layers(self):
      class MyLayer(tf_keras.layers.Layer):
          def __init__(self, sublayers, **kwargs):
              super().__init__(**kwargs)
              self.sublayers = sublayers
  
          def call(self, x):
              prev_input = x
              for layer in self.sublayers:
                  prev_input = layer(prev_input)
              return prev_input
  
          def get_config(self):
              config = super().get_config()
              config[
                  "sublayers"
              ] = tf_keras.saving.serialize_keras_object(self.sublayers)
              return config
  
          @classmethod
          def from_config(cls, config):
              config[
                  "sublayers"
              ] = tf_keras.saving.deserialize_keras_object(
                  config["sublayers"]
              )
              return cls(**config)
  
      @tf_keras.saving.register_keras_serializable(package="Foo")
      class RegisteredSubLayer(layers.Layer):
          def call(self, x):
              return x
  
      layer = MyLayer(
          [
              tf_keras.layers.Dense(2, name="MyDense"),
              RegisteredSubLayer(name="MySubLayer"),
          ]
      )
      tf_keras_model = tf_keras.Sequential([layer])
  
      x = np.random.random((4, 2))
      ref_output = tf_keras_model(x)
  
      # Save TF Keras model to H5 file
      temp_filepath = os.path.join(self.get_temp_dir(), "model.h5")
      tf_keras_model.save(temp_filepath)
  
      # Re-implement in Keras 3
      class MyLayer(layers.Layer):
          def __init__(self, sublayers, **kwargs):
              super().__init__(**kwargs)
              self.sublayers = sublayers
  
          def call(self, x):
              prev_input = x
              for layer in self.sublayers:
                  prev_input = layer(prev_input)
              return prev_input
  
          def get_config(self):
              config = super().get_config()
              config[
                  "sublayers"
              ] = serialization_lib.serialize_keras_object(
                  self.sublayers
              )
              return config
  
          @classmethod
          def from_config(cls, config):
              config[
                  "sublayers"
              ] = serialization_lib.deserialize_keras_object(
                  config["sublayers"]
              )
              return cls(**config)
  
      # Re-implement and re-register in Keras 3
      @object_registration.register_keras_serializable(package="Foo")
      class RegisteredSubLayer(layers.Layer):
          def call(self, x):
              return x
  
      # Load in Keras 3
      loaded_model = legacy_h5_format.load_model_from_hdf5(
          temp_filepath, custom_objects={"MyLayer": MyLayer}
      )
      loaded_layer = loaded_model.layers[0]
      output = loaded_model(x)
  
      # Ensure nested layer structure
      self.assertIsInstance(loaded_layer.sublayers[0], layers.Dense)
      self.assertEqual(loaded_layer.sublayers[0].name, "MyDense")
      self.assertIsInstance(
          loaded_layer.sublayers[1], RegisteredSubLayer
      )
      self.assertEqual(loaded_layer.sublayers[1].name, "MySubLayer")
  
      # Compare output
      self.assertAllClose(ref_output, output, atol=1e-5)
  
imports: import os
  import numpy
  import pytest
  import keras
  from keras import layers
  from keras import models
  from keras import ops
  from keras import testing
  from keras.legacy.saving import legacy_h5_format
  from keras.saving import object_registration
  from keras.saving import serialization_lib
label_desc: Use image node_id 9 for calling the LegacyH5BackwardsCompatTest obj's underlying member method code with example usage: obj.DISABLED_test_nested_layers() and returns: prev_input, config, cls, x, prev_input, config, cls, x