Keras Tuner on autoencoder - Add condition : first hidden layer units greater than or equal next hidden layer units

I want to use Keras-tuner to tune an autoencoder hyperparameters. It is a symetric AE with two layers. I want the number of units in the first layer always greater than or equal the units in the second layer. But I don't know how implement it with keras-tuner. If someone can help, it would be very great. Thank you in advance.

class DAE(tf.keras.Model):
    '''
    A DAE model
    '''

    def __init__(self, hp, **kwargs):
        '''
        DAE instantiation
        args :
            hp  : Tuner
            input_dim  : input dimension
        return:
            None
        '''
        super(DAE, self).__init__(**kwargs)
        input_dim = 15
        latent_dim = hp.Choice("latent_space", [2,4,8])
        units_0 = hp.Choice("units_0", [8, 16, 32, 64])
        units_1 = hp.Choice("units_1", [8, 16, 32, 64])
        
        for i in [8, 16, 32, 64]:
            with hp.conditional_scope("units_0", [i]):
                if units_0 == i:
                    ......? # units_1 should be <= i
                    
        dropout = hp.Choice("dropout_rate", [0.1, 0.2, 0.3, 0.4, 0.5])

        inputs    = tf.keras.Input(shape = (input_dim,))
        x         = layers.Dense(units_0, activation="relu")(inputs)
        x         = layers.Dropout(dropout)(x)
        x         = layers.Dense(units_1, activation="relu")(x)
        x         = layers.Dropout(dropout)(x)
        z         = layers.Dense(latent_dim)(x)
        self.encoder = tf.keras.Model(inputs, z, name="encoder")
        
        inputs  = tf.keras.Input(shape=(latent_dim,))
        x       = layers.Dense(units_1, activation="relu")(inputs)
        x       = layers.Dropout(dropout)(x)
        x       = layers.Dense(units_0, activation="relu")(x)
        x       = layers.Dropout(dropout)(x)
        outputs = layers.Dense(input_dim, activation="linear")(x)
        self.decoder = tf.keras.Model(inputs, outputs, name="decoder")```


See above my code. It's a denoising autoencoder class

I found the solution. We need to create differents units_1 for for each units_O values

class DAE(tf.keras.Model):
'''
A DAE model
'''

def __init__(self, hp, training=None, **kwargs):
    '''
    DAE instantiation
    args :
        hp  : Tuner
        input_dim  : input dimension
    return:
        None
    '''
    super(DAE, self).__init__(**kwargs)
    self.input_dim = 15
    l_units = [16, 32, 64, 128]
    latent_dim = hp.Choice("latent_space", [2,4,8])
    units_0 = hp.Choice("units_0", l_units)
    dropout_0 = hp.Choice("dropout_rate_0", [0.1, 0.2, 0.3, 0.4, 0.5])
    dropout_1 = hp.Choice("dropout_rate_1", [0.1, 0.2, 0.3, 0.4, 0.5])
    
    for i in l_units:
        name = "units_1_%d" % i  # generates unique name for each hp.Int object
        with hp.conditional_scope("units_0", [i]):
            if units_0 == i:
                locals()[name] = hp.Int(name, min_value = 8, max_value = i, step = 2, sampling = "log" )

                inputs    = tf.keras.Input(shape = (self.input_dim,))
                x         = layers.Dense(units_0, activation="relu")(inputs)
                x         = layers.Dropout(dropout_0)(x, training=training)  
                x         = layers.Dense(locals()[name], activation="relu")(x)
                x         = layers.Dropout(dropout_1)(x, training=training)  
                z         = layers.Dense(latent_dim)(x)
                self.encoder = tf.keras.Model(inputs, z, name="encoder")

                inputs  = tf.keras.Input(shape=(latent_dim,))
                x       = layers.Dense(locals()[name], activation="relu")(inputs)
                x       = layers.Dropout(dropout_1)(x, training=training)  
                x       = layers.Dense(units_0, activation="relu")(x)
                x       = layers.Dropout(dropout_0)(x, training=training)  
                outputs = layers.Dense(self.input_dim, activation="linear")(x)
                self.decoder = tf.keras.Model(inputs, outputs, name="decoder")