Python 用于模型创建的TensorFlow子类化中层的可重用性

因此，我目前正在学习如何使用子类化在TensorFlow中创建模型.根据本教程，以下代码片段应该可以完美运行:

#Defining the class
class FeatureExtractor(Layer):
    def __init__(self):
        super().__init__()

        self.conv_1 = Conv2D(filters = 6, kernel_size = 4, padding = "valid", activation = "relu")
        self.batchnorm_1 = BatchNormalization()
        self.maxpool_1 = MaxPool2D(pool_size = 2, strides=2)

        self.conv_2 = Conv2D(filters = 16, kernel_size = 4, padding = "valid", activation = "relu")
        self.batchnorm_2 = BatchNormalization()
        self.maxpool_2 = MaxPool2D(pool_size = 2, strides=2)


    def call(self, x):
        x = self.conv_1(x)
        x = self.batchnorm_1(x)
        x = self.maxpool_1(x)

        x = self.conv_2(x)
        x = self.batchnorm_2(x)
        x = self.maxpool_2(x)

        return x

#Calling and using the class
feature_extractor = FeatureExtractor()

func_input = Input(shape=(IMG_SIZE, IMG_SIZE, 3), name="Input_Image")

x = feature_extractor(func_input)

而且它确实运行得无懈可击.但后来我意识到，在__init__()中，BatchNormalization()和MaxPool2D()看起来是一样的，但被定义了两次，而不是被重复使用，所以我做了一些研究和asked on Stackoverflow，得出了when layers are called in 104, they adapt to the dimension of the input, and then keep that dimension for later calls的结论，这阻止了这些层能够被重复使用.

但正如Fynn在我的previous question中指出的那样，maxpool个成员实际上可以重复使用，而batchnorm个成员不能.那么，一些层适应输入的维度(比如batchnorm)，而另一些不适应(比如maxpool)？或者是这些层的某个属性导致了这种行为？(我可以在文档中查找)