message: Add custom_objects in the function layer_test
  layer test for custom layer
  Usage:
  ```
  testing_utils.layer_test(
  SyncBatchNormalization,
  # keras.layers.BatchNormalization,
  kwargs={
  'momentum': 0.9,
  'epsilon': 0.1,
  'gamma_regularizer': keras.regularizers.l2(0.01),
  'beta_regularizer': keras.regularizers.l2(0.01)
  },
  input_shape=(3, 4, 2),
  custom_objects={'SyncBatchNormalization': SyncBatchNormalization})
  ```
diff: @@ -75,7 +75,7 @@ def get_test_data(train_samples,
  def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None,
  input_data=None, expected_output=None,
  expected_output_dtype=None, expected_output_shape=None,
  - validate_training=True, adapt_data=None):
  + validate_training=True, adapt_data=None, custom_objects=None):
  """Test routine for a layer with a single input and single output.
  Arguments:
  @@ -93,6 +93,8 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None,
  string or integer values.
  adapt_data: Optional data for an 'adapt' call. If None, adapt() will not
  be tested for this layer. This is only relevant for PreprocessingLayers.
  + custom_objects: Optional Objects for your custom layer. If you write a
  + your layer, you can use this variable.
  Returns:
  The output data (Numpy array) returned by the layer, for additional
  @@ -196,7 +198,8 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None,
  # test serialization, weight setting at model level
  model_config = model.get_config()
  - recovered_model = keras.models.Model.from_config(model_config)
  + recovered_model = keras.models.Model.from_config(
  + model_config, custom_objects)
  if model.weights:
  weights = model.get_weights()
  recovered_model.set_weights(weights)
  @@ -250,7 +253,8 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None,
  # test serialization, weight setting at model level
  model_config = model.get_config()
  - recovered_model = keras.models.Sequential.from_config(model_config)
  + recovered_model = keras.models.Sequential.from_config(
  + model_config, custom_objects)
  if model.weights:
  weights = model.get_weights()
  recovered_model.set_weights(weights)
  