keras-tuner: KeyError when using conditional hyperparameters

Describe the bug When I use parent_name and parent_valus I always get a KeyError when building a model.

Invalid model 5/5
Traceback (most recent call last):
  File "/Users/douira/Documents/dev/uni/bachelorarbeit/tf-time-series/venv/lib/python3.8/site-packages/keras_tuner/engine/hypermodel.py", line 127, in build
    model = self.hypermodel.build(hp)
  File "keras-tuner-test.py", line 22, in model_builder
    dense_units1 = hp.Int(
  File "/Users/douira/Documents/dev/uni/bachelorarbeit/tf-time-series/venv/lib/python3.8/site-packages/keras_tuner/engine/hyperparameters.py", line 850, in Int
    return self._retrieve(hp)
  File "/Users/douira/Documents/dev/uni/bachelorarbeit/tf-time-series/venv/lib/python3.8/site-packages/keras_tuner/engine/hyperparameters.py", line 707, in _retrieve
    return self.values[hp.name]
KeyError: 'dense_units1'

To Reproduce Use keras-tuner 1.0.4: https://colab.research.google.com/drive/1dlys0Dmpt9hjLkKOP62SfhYnIQmOgVxy?usp=sharing

import keras_tuner as kt
from tensorflow import keras

def model_builder(hp):
    dense_layers = hp.Int("dense_layers", min_value=0, max_value=2, step=1)

    dense_units1 = hp.Int(
        "dense_units1",
        min_value=16,
        max_value=512,
        step=16,
        parent_name="dense_layers",
        parent_values=[1, 2],
    )

    return keras.Sequential()

tuner = kt.RandomSearch(
    model_builder,
    objective="val_accuracy",
    directory="./model_tuning",
    max_trials=1,
)

Expected behavior The model should build normally. This worked on keras-tuner 1.0.3. It does not work (this bug happens) on keras-tuner 1.0.4.

Additional context If there is any way of using conditional hyperparameters on keras-tuner while avoiding this bug I’d be interested to hear about it. Otherwise I’ll downgrade my version of keras-tuner until this issue is resolved somehow. Thank you for the great work on this project!

Would you like to help us fix it? I don’t know why it’s happening. Maybe I’m doing something wrong. If this actually is a bug, then looking at what changed between keras-tuner 1.0.3 and 1.0.4 is probably a good idea. (the Google Collab confirms this difference)