[Python] Cannot see all `Dense` layer info from `search_space_summary()` when using...

I am trying to use keras-tuner to tune hyperparameters, like

!pip install keras-tuner --upgrade
import keras_tuner as kt
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.optimizers import Adam

def build_model(hp):
model = Sequential([
Flatten(input_shape=(28, 28)),
Dense(units= hp.Int('units', min_value = 16, max_value = 64, step = 16), activation='relu'),
Dense(units = hp.Int('units', min_value = 8, max_value = 20, step = 2), activation='softmax')
])

model.compile(
optimizer=Adam(learning_rate=hp.Float('learning_rate', min_value=1e-4, max_value=1e-2, sampling='LOG')),
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
return model

# Create a RandomSearch Tuner
tuner = kt.RandomSearch(
build_model,
objective='val_accuracy',
max_trials=10,
executions_per_trial=2
)

# Display a summary of the search space
tuner.search_space_summary()


shows

Search space summary
Default search space size: 2
units (Int)
{'default': None, 'conditions': [], 'min_value': 16, 'max_value': 64, 'step': 16, 'sampling': 'linear'}
learning_rate (Float)
{'default': 0.0001, 'conditions': [], 'min_value': 0.0001, 'max_value': 0.01, 'step': None, 'sampling': 'log'}


However, when checking the search_space_summary() output, there is only the 1st Dense layer is shown in the summary, while the information about the 2nd Dense layer, i.e., Dense(units = hp.Int('units', min_value = 8, max_value = 20, step = 2), activation='softmax'), is not seen.

Did I misconfigured something or it is supposed to yield the output like that? Could anyone help me to understand why it outputs the summary like this?

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