如何使用tensorflow在迁移学习中在模型之前添加几层

import tensorflow as tf
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Model

base_model = tf.keras.applications.ResNet50(include_top=False, weights='imagenet', input_shape=(224, 224, 3))

base_model.trainable = False

inputs = tf.keras.Input(shape=(224, 224, 3))
x = base_model(inputs)
x = tf.keras.layers.Flatten()(x)
x = Dense(256, activation='relu')(x)
x = Dense(128, activation='relu')(x)
outputs = Dense(num_classes, activation='softmax')(x)

model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

model.fit(train_dataset, epochs=10, validation_data=val_dataset)