[PYTHON] Error of tf.function-decorated function tried to create variables on non-first call. In tensorflow.keras

Sometimes it tends to get clogged, so in a memorandum

Error part

class ResNet50(Model):
    def __init__(self, stride: int = 1, *args, **kwargs):
        super(ResNet50, self).__init__(*args, **kwargs)
        self.stride = stride
        self.avgpool = AveragePooling2D()
        self.maxpool = MaxPool2D(padding='same')
        self.ResBlocks: List[Layers] = [] 
        
        self.softmax = Softmax()
        self.dense = Dense(1, activation='sigmoid')

    def call(self, inputs):
        conv_1 = self.conv(inputs)
        maxpooled = self.maxpool(conv_1)
        layers_num = [3, 4, 6, 3]
        for i in range(len(layers_num)):
            for _ in range(layers_num[i]):
                if i==0 and u==0:
                   self.ResBlocks.append(Residual_Block(filters_num=4 * 2 ** (i))(maxpooled))
                else:
                self.ResBlocks.append(Residual_Block(filters_num=4 * 2 ** (i))(self.ResBlocks[-1])))
        
        avgpooled = self.avgpool(maxpooled)
        value = self.dense(avgpooled)
        return avgpooled, value

I did something like that and got a tf.function-decorated function tried to create variables on non-first call. If you look it up, the tensorflow declaration variable will be newly declared.

After resolution

class ResNet50(Model):
    def __init__(self, stride: int = 1, *args, **kwargs):
        super(ResNet50, self).__init__(*args, **kwargs)
        self.stride = stride
        self.avgpool = AveragePooling2D()
        self.maxpool = MaxPool2D(padding='same')
        self.ResBlocks: List[Layers] = [] 
        layers_num = [3, 4, 6, 3]
        for i in range(len(layers_num)):
            for _ in range(layers_num[i]):
                self.ResBlocks.append(Residual_Block(filters_num=4 * 2 ** (i)))
        self.conv = Conv2D(filters=16, kernel_size=7, strides=self.stride, padding='same')
        self.softmax = Softmax()
        self.dense = Dense(1, activation='sigmoid')
    def call(self, inputs):
        conv_1 = self.conv(inputs)
        maxpooled = self.maxpool(conv_1)
        for layer in self.ResBlocks:
            maxpooled = layer(maxpooled)
        avgpooled = self.avgpool(maxpooled)
        value = self.dense(avgpooled)
        return avgpooled, value

Then it was fixed. The cause was that when declaring a layer in the Model specification of tensorflow.keras, it must be in the init part. I completely forgot. Is the declaration of variables related all over the place? I would like to find out even when I have time next time.