message: Add more docs on using TFLite through C++
diff: @@ -262,9 +262,24 @@ Note that the `0.1.100` version here is purely for the sake of
  testing/development. With the local AAR installed, you can use the standard
  [TensorFlow Lite Java inference APIs](../guide/inference.md) in your app code.
  -##### Build C++ libraries
  +## Build Android app using C++
  -If you want to use TFLite through C++ libraries, you can build the shared
  +There are two ways to use TFLite through C++ if you build your app with the NDK:
  +
  +### Use TFLite C API
  +
  +This is the *recommended* approach. Download the
  +[TensorFlow Lite AAR hosted at JCenter](https://bintray.com/google/tensorflow/tensorflow-lite),
  +rename it to `tensorflow-lite-*.zip`, and unzip it. You must include the three
  +header files in `headers/tensorflow/lite/c/` folder and the relevant
  +`libtensorflowlite_jni.so` dynamic library in `jni/` folder in your NDK project.
  +
  +The `c_api.h` header file contains basic documentation about using the TFLite C
  +API.
  +
  +### Use TFLite C++ API
  +
  +If you want to use TFLite through C++ API, you can build the C++ shared
  libraries:
  32bit armeabi-v7a:
  @@ -278,3 +293,9 @@ bazel build -c opt --config=android_arm //tensorflow/lite:libtensorflowlite.so
  ```sh
  bazel build -c opt --config=android_arm64 //tensorflow/lite:libtensorflowlite.so
  ```
  +
  +Currently, there is no straightforward way to extract all header files needed,
  +so you must include all header files in `tensorflow/lite/` from the TensorFlow
  +repository. Additionally, you will need header files from
  +[FlatBUffers](https://github.com/google/flatbuffers) and
  +[Abseil](https://github.com/abseil/abseil-cpp).
  