keras define a trainable variable for add or matmul - tensorflow

Question

I have some problems in use tf.keras to build model. Now I want to define a trainbale weight tensor with shape(64, 128), which similar to tf.get_variable. However I can't achieve it.
In the past, I have try many methods.But I want to look for easily method.
inputs = tf.keras.Input((128,))
weights = tf.Variable(tf.random.normal((64, 128)))
output = tf.keras.layers.Lambda(lambda x: tf.matmul(x, tf.transpose(weights)))(inputs)
model = tf.keras.Model(inputs, output)
model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_10 (InputLayer) (None, 128) 0
_________________________________________________________________
lambda_2 (Lambda) (None, 64) 0
=================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
The defined weights is not trainable.
In addition, I know Dense can get trained matrix weights and bias. But if I want add a bias, I can't use Dense.
However, I have to use add_weights in custome layer, for example:
class Bias(keras.layers.Layer):
def build(self, input_shape):
self.bias = self.add_weight(shape=(64, 128), initializer='zeros', dtype=tf.float32, name='x')
self.built = True
def call(self, inputs):
return inputs + self.bias
inputs = Input(shape=(64, 128))
outputs = Bias()(inputs)
model = Model(inputs=inputs, outputs=outputs)
model.summary()
Layer (type) Output Shape Param #
=================================================================
input_11 (InputLayer) (None, 64, 128) 0
_________________________________________________________________
bias_5 (Bias) (None, 64, 128) 8192
=================================================================
Total params: 8,192
Trainable params: 8,192
Non-trainable params: 0
Is there any more easily method to define a trainable variable ？