Clarify experimental API

Author: saxenasaurabh

rfcs/20201201-cpp-gradients.md

@@ -10,7 +10,7 @@
 
 ## Objective
 
-We propose performing gradient computation entirely in C++. This aligns with TensorFlow Core team’s vision of providing first-class C++ APIs for building ML models. We mainly focus on reverse-mode autodiff here and leave forward-mode AD as future work. A C API for this implementation is also left as future work but we imagine it to be a straightforward wrapper over the provided C++ APIs.
+We propose performing gradient computation entirely in C++. We mainly focus on reverse-mode autodiff here and leave forward-mode AD as future work. A C API for this implementation is also left as future work but we imagine it to be a straightforward wrapper over the provided C++ APIs. The APIs discussed here are experimental and hence do not have any backwards compatibility guarantees yet.
 
 
 ## Motivation
@@ -308,9 +308,6 @@ Status TapeOperation::Execute(absl::Span<AbstractTensorHandle*> retvals,
 This way the same C++ gen\_ops code can be used to execute ops with/without a tape by simply wrapping the current execution context in a `TapeContext`.
 
 
-Note: This interface is subject to change.
-
-
 **Some details on memory management**
 
 `AbstractTensorHandle` provides `Ref` and `Unref` methods which can be used to manage its lifecycle. Gradient functions and the tape follow these guidelines for memory safety: