In addition, as per @markdaoust 's suggestion, check out:

  
      

      TensorFlow
  

  
    

Signatures in TensorFlow Lite 1



  

  
    
    
  

  



TensorFlow inference APIs support the signature-based executions:

Accessing the input/output tensors through the names of the inputs and outputs, specified by the signature.
Running each entry point of the graph separately, identified by the signature key.
Support for the SavedModel’s initialization procedure.

Java, C++ and Python language bindings are currently available.

Java:
try (Interpreter interpreter = new Interpreter(file_of_tensorflowlite_model)) {
  // Run encoding signature.
  Map<String, Object> inputs = new HashMap<>();
  inputs.put("x", input);
  Map<String, Object> outputs = new HashMap<>();
  outputs.put("encoded_result", encoded_result);
  interpreter.runSignature(inputs, outputs, "encode");

  // Run decoding signature.
  Map<String, Object> inputs = new HashMap<>();
  inputs.put("x", encoded_result);
  Map<String, Object> outputs = new HashMap<>();
  outputs.put("decoded_result", decoded_result);
  interpreter.runSignature(inputs, outputs, "decode");
}