How to use keras layers in custom keras layer

In the TF2 custom layer Guide, they "recommend creating such sublayers in the __init__ method (since the sublayers will typically have a build method, they will be built when the outer layer gets built)." So just move the creation of self.fc into __init__ will give what you want.

class MyDenseLayer(tf.keras.layers.Layer):
  def __init__(self, num_outputs):
    super(MyDenseLayer, self).__init__()
    self.num_outputs = num_outputs
    self.fc = tf.keras.layers.Dense(self.num_outputs)

  def build(self, input_shape):
    self.built = True

  def call(self, input):
    return self.fc(input)

input = tf.keras.layers.Input(shape = (16,))
output = MyDenseLayer(10)(input)
model = tf.keras.Model(inputs = [input], outputs = [output])
model.summary()

Output:

Model: "model_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         [(None, 16)]              0         
_________________________________________________________________
my_dense_layer_2 (MyDenseLay (None, 10)                170       
=================================================================
Total params: 170
Trainable params: 170
Non-trainable params: 0

It's much more comfortable and concise to put existing layers in the tf.keras.models.Model class. If you define non-custom layers such as layers, conv2d, the parameters of those layers are not trainable by default.

class MyDenseLayer(tf.keras.Model):
  def __init__(self, num_outputs):
    super(MyDenseLayer, self).__init__()
    self.num_outputs = num_outputs
    self.fc = tf.keras.layers.Dense(num_outputs)

  def call(self, input):
    return self.fc(input)

  def compute_output_shape(self, input_shape):
    shape = tf.TensorShape(input_shape).as_list()
    shape[-1] = self.num_outputs
    return tf.TensorShape(shape)

layer = MyDenseLayer(10)

Check this tutorial: https://www.tensorflow.org/guide/keras#model_subclassing

If you look at the documentation for how to add custom layers, they recommend that you use the .add_weight(...) method. This method internally places all weights in self._trainable_weights. So to do what you want, you mush first define the keras layers you want to use, build them, copy the weights and then build your own layer. If I update your code it should be something like

class mylayer(tf.keras.layers.Layer):
    def __init__(self, num_outputs, num_outputs2):
        self.num_outputs = num_outputs
        super(mylayer, self).__init__()

    def build(self, input_shape):
        self.fc = tf.keras.layers.Dense(self.num_outputs)
        self.fc.build(input_shape)
        self._trainable_weights = self.fc.trainable_weights
        super(mylayer, self).build(input_shape)

    def call(self, input):
        return self.fc(input)

layer = mylayer(10)
input = tf.keras.layers.Input(shape=(16, ))
output = layer(input)
model = tf.keras.Model(inputs=[input], outputs=[output])
model.summary()

You should then get what you want