Get intermediate output of layer (not Model!)

We want to re-use an existing keras layer, but return an intermediate value of the call function.
To be precise we aim to re-use the ResNet class of SimCLR, which equals to:

class Resnet(tf.keras.layers.Layer):
  def call(self, inputs, training):
    for layer in self.initial_conv_relu_max_pool:
      inputs = layer(inputs, training=training)

    for i, layer in enumerate(self.block_groups):      
      inputs = layer(inputs, training=training)

    inputs = tf.reduce_mean(inputs, [1, 2])
    inputs = tf.identity(inputs, 'final_avg_pool')
    return inputs

We want to obtain the inputs before the reduce_mean function.
If this was a keras Model we could do something like model.get_layer(index=X).output.

Keras Layers do have submodules, and we could identify the correct submodule (resnet_model.submodules[8].name returns block_group4 as expected). However, resnet_model.submodules[8].output yields an

AttributeError: Layer block_group4 has no inbound nodes.

Is the only way to subclass and redefine call? Or is there another way to get the output of a submodule / intermediate value of the layer?

Similar issue Tensorflow 2 Hub: How can I obtain the output of an intermediate layer? - Stack Overflow

Thanks for thinking along.
For TF2 Hub the proposed solution on Stack Overflow is to use:

resnet = hub.Module(...)
outputs = resnet(..., as_dict=True)

However, unfortunately, tf.keras.layers.Layer does not accept as_dict in the call signature, ie:

from simclr.tf2 import resnet as simclr_resnet
resnet_model = simclr_resnet.resnet(50, 1)
outputs = resnet_model(..., as_dict=True)

Yields an TypeError: got an unexpected keyword argument 'as_dict'