conditional_scope might not be correctly exemplified.

[scrovy]

This code does not illustrate the utility of conditional_scope according to what is described above it. Since no hyperparameters are created within the with hp.conditional_scope("model_type", ...): scopes, the code will behave the same as if the scopes were not there at all. Could you either improve the code by inserting some hp.Int, hp.Choice, etc to the body of the scopes or clarify if I misunderstood the expected behavior of conditional_scope? Thanks in advance.

keras-tuner/keras_tuner/engine/hyperparameters/hyperparameters.py

Lines 105 to 121 in 0dd114d

	```python
	def MyHyperModel(HyperModel):
	def build(self, hp):
	model = Sequential()
	model.add(Input(shape=(32, 32, 3)))
	model_type = hp.Choice("model_type", ["mlp", "cnn"])
	with hp.conditional_scope("model_type", ["mlp"]):
	if model_type == "mlp":
	model.add(Flatten())
	model.add(Dense(32, activation='relu'))
	with hp.conditional_scope("model_type", ["cnn"]):
	if model_type == "cnn":
	model.add(Conv2D(64, 3, activation='relu'))
	model.add(GlobalAveragePooling2D())
	model.add(Dense(10, activation='softmax'))
	return model
	```