[Doc] Remove trailing whitespaces (#13390)

doc/source/serve/advanced.rst

@@ -361,7 +361,7 @@ The following simple example will make the usage clear:
 The `reconfigure` method is called when the class is created if `user_config`
 is set.  In particular, it's also called when new replicas are created in the
 future, in case you decide to scale up your backend later.  The
-`reconfigure` method is also called each time `user_config` is updated via 
+`reconfigure` method is also called each time `user_config` is updated via
 :mod:`client.update_backend_config <ray.serve.api.Client.update_backend_config>`.
 
 Dependency Management

doc/source/serve/index.rst

@@ -33,7 +33,7 @@ Ray Serve can be used in two primary ways to deploy your models at scale:
   Chat with Ray Serve users and developers on our `community Slack <https://forms.gle/9TSdDYUgxYs8SA9e8>`_ in the #serve channel and on our `forum <https://discuss.ray.io/>`_!
 
 .. note::
-  Starting with Ray version 1.2.0, Ray Serve backends take in a Starlette Request object instead of a Flask Request object.  
+  Starting with Ray version 1.2.0, Ray Serve backends take in a Starlette Request object instead of a Flask Request object.
   See the `migration guide <https://docs.google.com/document/d/1CG4y5WTTc4G_MRQGyjnb_eZ7GK3G9dUX6TNLKLnKRAc/edit?usp=sharing>`_ for details.
 
 Ray Serve Quickstart
@@ -98,7 +98,7 @@ or head over to the :doc:`tutorials/index` to get started building your Ray Serv
 For more, see the following blog posts about Ray Serve:
 
 - `How to Scale Up Your FastAPI Application Using Ray Serve <https://medium.com/distributed-computing-with-ray/how-to-scale-up-your-fastapi-application-using-ray-serve-c9a7b69e786>`_ by Archit Kulkarni
-- `Machine Learning is Broken <https://medium.com/distributed-computing-with-ray/machine-learning-serving-is-broken-f59aff2d607f>`_ by Simon Mo 
-- `The Simplest Way to Serve your NLP Model in Production with Pure Python <https://medium.com/distributed-computing-with-ray/the-simplest-way-to-serve-your-nlp-model-in-production-with-pure-python-d42b6a97ad55>`_ by Edward Oakes and Bill Chambers 
+- `Machine Learning is Broken <https://medium.com/distributed-computing-with-ray/machine-learning-serving-is-broken-f59aff2d607f>`_ by Simon Mo
+- `The Simplest Way to Serve your NLP Model in Production with Pure Python <https://medium.com/distributed-computing-with-ray/the-simplest-way-to-serve-your-nlp-model-in-production-with-pure-python-d42b6a97ad55>`_ by Edward Oakes and Bill Chambers
 
 

doc/source/serve/key-concepts.rst

@@ -19,8 +19,8 @@ Backends
 Backends define the implementation of your business logic or models that will handle requests when queries come in to :ref:`serve-endpoint`.
 In order to support seamless scalability backends can have many replicas, which are individual processes running in the Ray cluster to handle requests.
 To define a backend, first you must define the "handler" or the business logic you'd like to respond with.
-The handler should take as input a `Starlette Request object <https://www.starlette.io/requests/>`_ and return any JSON-serializable object as output.  For a more customizable response type, the handler may return a 
-`Starlette Response object <https://www.starlette.io/responses/>`_.  
+The handler should take as input a `Starlette Request object <https://www.starlette.io/requests/>`_ and return any JSON-serializable object as output.  For a more customizable response type, the handler may return a
+`Starlette Response object <https://www.starlette.io/responses/>`_.
 
 A backend is defined using :mod:`client.create_backend <ray.serve.api.Client.create_backend>`, and the implementation can be defined as either a function or a class.
 Use a function when your response is stateless and a class when you might need to maintain some state (like a model).

doc/source/serve/tutorials/web-server-integration.rst

@@ -37,7 +37,7 @@ Now you can query your web server, for example by running the following in anoth
 .. code-block:: bash
 
   curl "http://127.0.0.1:8000/generate?query=Hello%20friend%2C%20how"
-  
+
 The terminal should then print the generated text:
 
 .. code-block:: bash
@@ -66,7 +66,7 @@ Here's how to run this example:
 
 1. Run ``ray start --head`` to start a local Ray cluster in the background.
 
-2. In the directory where the example files are saved, run ``python deploy_serve.py`` to deploy our Ray Serve endpoint.  
+2. In the directory where the example files are saved, run ``python deploy_serve.py`` to deploy our Ray Serve endpoint.
 
 .. note::
   Because we have omitted the keyword argument ``route`` in ``client.create_endpoint()``, our endpoint will not be exposed over HTTP by Ray Serve.