为什么在构建时在Keras图层中调用call方法

import tensorflow as tf

class MyLayer(tf.keras.layers.Layer):
    def __init__(self, units):
        super(MyLayer, self).__init__()
        self.units = units

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

    def call(self, inputs):
        return tf.matmul(inputs, self.w) + self.b

# 创建一个模型
model = tf.keras.Sequential([
    MyLayer(64),
    tf.keras.layers.ReLU(),
    MyLayer(10),
    tf.keras.layers.Softmax()
])

# 使用模型进行前向传播
inputs = tf.random.normal(shape=(32, 100))
outputs = model(inputs)