why can we use parameter "input_shape = (2,)" which undefined in __init__?

class Linear(layers.Layer):
    def __init__(self, units=32, **kwargs):
#         super(Linear, self).__init__(**kwargs)
        super().__init__(**kwargs)
        self.units = units
    
    # The trainable parameters are defined in build method
    # Since we do not need the input_shape except the build function,
    # we do not need to store then in the __init__ function
    def build(self, input_shape): 
        self.w = self.add_weight("w",shape=(input_shape[-1], self.units),
                                 initializer='random_normal',
                                 trainable=True) # Parameter named "w" is compulsory or an error will be thrown out
        self.b = self.add_weight("b",shape=(self.units,),
                                 initializer='random_normal',
                                 trainable=True)
        super().build(input_shape) # Identical to self.built = True

    # The logic of forward propagation is defined in call method, and is called by __call__ method
    @tf.function
    def call(self, inputs): 
        return tf.matmul(inputs, self.w) + self.b
    
    # Use customized get-config method to save the model as h5 format, specifically for the model composed through Functional API with customized Layer
    def get_config(self):  
        config = super().get_config()
        config.update({'units': self.units})
        return config

tf.keras.backend.clear_session()

model = models.Sequential()
# Note: the input_shape here will be modified by the model, so we don't have to fill None in the dimension representing the number of samples.
model.add(Linear(units = 1,input_shape = (2,)))  
print("model.input_shape: ",model.input_shape)
print("model.output_shape: ",model.output_shape)
model.summary()