在函数式Keras模型中，如何将前几层的权重作为输入传递给客户层的调用函数？

import tensorflow as tf
from tensorflow.keras.layers import Layer

class CustomLayer(Layer):
    def __init__(self, units):
        super(CustomLayer, self).__init__()
        self.units = units

    def build(self, input_shape):
        self.kernel = self.add_weight(shape=(input_shape[-1], self.units),
                                      initializer='random_normal',
                                      trainable=True)

    def call(self, inputs):
        previous_weights = inputs[0]
        previous_outputs = inputs[1]

        # 使用前几层的权重和输出进行计算
        # ...

        return outputs

# 假设前几层的输出张量为previous_outputs
previous_outputs = ...

# 创建前几层的权重张量
previous_weights = []
for layer in previous_layers:
    previous_weights.append(layer.get_weights())

# 创建客户层的调用函数
custom_layer = CustomLayer(units=32)

# 调用客户层的__call__方法，传递前几层的权重和输出作为输入
outputs = custom_layer.call([previous_weights, previous_outputs])