* `tf.lite`
    * Add experimental supports conversion of models that may be larger than 2GB before buffer deduplication

### Bug Fixes and Other Changes

* `tf.py_function` and `tf.numpy_function` can now be used as function decorators for clearer code:
   ```
   @tf.py_function(Tout=tf.float32)
   def my_fun(x):

One of the **biggest benefits** is that now you can create `Annotated` dependencies that are then shared by multiple *path operation functions*, this will allow you to **reduce** a lot of **code duplication** in your codebase, while keeping all the support from editors and tools.

For example, you could have code like this:

```Python
def get_current_user(token: str):
    # authenticate user