Q:

Customize Input to Tensorflow Hub module

I know how to load a pre-trained image models from Tensorflow Hub.  like so:  
#load model
image_module = hub. Module('https://tfhub.dev/google/imagenet/mobilenet_v2_035_128/feature_vector/2')

#get predictions
features = image_module(batch_images)

I also know how to customize the output of this model (fine-tune on new dataset).  The existing Modules expect input batch_images to be a RGB image tensor. 
My question: Instead of the input being a RGB image of certain dimensions, I would like to use a tensor (dim 20x20x128, from a different model) as input to the Hub model.  This means I need to by-passing the initial layers of the tf-hub model definition (i don't need them).  Is this possible in tf-hub module api's?  Documentation is not clear on this aspect. 
p.s.:  I can do this easily be defining my own layers but trying to see if i can use the Tf-Hub API's. 

A:

The existing https://tfhub.dev/google/imagenet/... modules do not support this. 
Generally speaking, the hub. Module format allows multiple signatures (that is, combinations of input/output tensors; think feeds and fetches as in tf.Session.run()).  So module publishers can arrange for that if there is a common usage pattern they want to support. 
But for free-form experimentation at this level of sophistication, you are probably better off directly using and tweaking the code that defines the models, such as TF Slim (for TF1.x) or Keras Applications (also for TF2).  Both provide Imagenet-pretrained checkpoints for downloading and restoring on the side.