如何将fix超参数作为变量传递给Keras-Tuner？

pip install keras-tuner

import tensorflow as tf
from kerastuner import HyperModel
from kerastuner.tuners import RandomSearch

from kerastuner.engine.hyperparameters import HyperParameters

hp = HyperParameters()
hp.Fixed('learning_rate', value=0.001)

class MyHyperModel(HyperModel):
    def __init__(self, input_shape, num_classes, hp):
        self.input_shape = input_shape
        self.num_classes = num_classes
        self.hp = hp

    def build(self, hp):
        model = tf.keras.Sequential()
        model.add(tf.keras.layers.Dense(hp.Int('units', 32, 512, 32), activation='relu'))
        model.add(tf.keras.layers.Dense(self.num_classes, activation='softmax'))
        model.compile(optimizer=tf.keras.optimizers.Adam(hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),
                      loss='sparse_categorical_crossentropy',
                      metrics=['accuracy'])
        return model

hypermodel = MyHyperModel(input_shape, num_classes, hp)

tuner = RandomSearch(
    hypermodel,
    objective='val_accuracy',
    max_trials=10,
    directory='my_dir',
    project_name='my_project'
)

tuner.search(x=train_features, y=train_labels,
             validation_data=(val_features, val_labels),
             epochs=10)