units present for non existing layers in keras tuner results for sequential model

I am using keras tuner for hyperparameter tuning of ANN model. I am changing the no of layers in the model between 2 and 5, and nodes between 10 and 30. I am using random search model of keras tuner to select the best performing models. When I observed the results, the top results are as follows:

Best val_mse So Far: 0.03936238835255305
Total elapsed time: 01h 23m 53s
Results summary
Results in project\Sd attempt10
Showing 10 best trials
Objective(name='val_mse', direction='min')
Trial summary
Hyperparameters:
num_layers: 3
units_0: 15
units_1: 15
learning_rate: 0.001
units_2: 10
Score: 0.03936238835255305
Trial summary
Hyperparameters:
num_layers: 3
units_0: 15
units_1: 25
learning_rate: 0.01
units_2: 20
units_3: 25
units_4: 10
Score: 0.03974008063475291

we can see that in the second trial summary, though no of layers are 3, units are present for 5 hidden layers.

I have also attached the code snippet for arriving the result.

def build_model(hp):
    model = keras.Sequential()
    for i in range(hp.Int('num_layers', 2, 5)):
        model.add(layers.Dense(units=hp.Int('units_' + str(i),
                                            min_value=10,
                                            max_value=30,
                                            step=5),
                               activation='relu'))
    model.add(layers.Dense(1, activation='linear'))
    model.compile(
        optimizer=keras.optimizers.Adam(
            hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),
        loss='mse',#'mean_absolute_error',
        metrics=['mse','mae'])#['mean_absolute_error'])
    return model


tuner = RandomSearch(
    build_model,
    objective='val_mse',
    max_trials=25,
    executions_per_trial=3,
    directory='project',
    project_name='Sd attempt10')
tuner.search(X_train, y_train,
             epochs=100,
             validation_data=(Xnew, yact))
print(tuner.results_summary())

Can some one explain this behavior with the results.