Shared input in Sagemaker inference pipeline models

I'm deploying a SageMaker inference pipeline composed of two PyTorch models (model_1 and model_2), and I am wondering if it's possible to pass the same input to both the models composing the pipeline.

What I have in mind would work more or less as follows

1. Invoke the endpoint sending a binary encoded payload (namely payload_ser), for example:

   client.invoke_endpoint(EndpointName=ENDPOINT,
                          ContentType='application/x-npy',
                          Body=payload_ser)
   

2. The first model parses the payload with inut_fn function, runs the predictor on it, and returns the output of the predictor. As a simplified example:

   def input_fn(request_body, request_content_type):
       if request_content_type == "application/x-npy":
           input = some_function_to_parse_input(request_body)
       return input
   
   def predict_fn(input_object, predictor):
       outputs = predictor(input_object)
       return outputs
   
   def output_fn(predictions, response_content_type):
       return json.dumps(predictions)
   

3. The second model gets as payload both the original payload (payload_ser) and the output of the previous model (predictions). Possibly, the input_fn function would be used to parse the output of model_1 (as in the "standard case"), but I'd need some way to also make the original payload available to model_2. In this way, model_2 will use both the original payload and the output of model_1 to make the final prediction and return it to whoever invoked the endpoint.

Any idea if this is achievable?