chore: import upstream snapshot with attribution

tensorflow/lite/g3doc/guide/build_arm.md

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+# Build TensorFlow Lite for ARM boards
+
+This page describes how to build the TensorFlow Lite libraries for ARM-based
+computers.
+
+TensorFlow Lite supports two build systems and supported features from each
+build system are not identical. Check the following table to pick a proper build
+system.
+
+Feature                                                                                   | Bazel                        | CMake
+----------------------------------------------------------------------------------------- | ---------------------------- | -----
+Predefined toolchains                                                                     | armhf, aarch64               | armel, armhf, aarch64
+Custom toolchains                                                                         | harder to use                | easy to use
+[Select TF ops](https://www.tensorflow.org/lite/guide/ops_select)                         | supported                    | not supported
+[GPU delegate](https://www.tensorflow.org/lite/performance/gpu)                           | only available for Android   | any platform that supports OpenCL
+XNNPack                                                                                   | supported                    | supported
+[Python Wheel](https://www.tensorflow.org/lite/guide/build_cmake_pip)                     | supported                    | supported
+[C API](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/c/README.md) | supported                    | [supported](https://www.tensorflow.org/lite/guide/build_cmake#build_tensorflow_lite_c_library)
+[C++ API](https://www.tensorflow.org/lite/guide/inference#load_and_run_a_model_in_c)      | supported for Bazel projects | supported for CMake projects
+
+## Cross-compilation for ARM with CMake
+
+If you have a CMake project or if you want to use a custom toolchain, you'd
+better use CMake for cross compilation. There is a separate
+[Cross compilation TensorFlow Lite with CMake](https://www.tensorflow.org/lite/guide/build_cmake_arm)
+page available for this.
+
+## Cross-compilation for ARM with Bazel
+
+If you have a Bazel project or if you want to use TF ops, you'd better use Bazel
+build system. You'll use the integrated
+[ARM GCC 8.3 toolchains](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/toolchains/embedded/arm-linux)
+with Bazel to build an ARM32/64 shared library.
+
+| Target Architecture | Bazel Configuration     | Compatible Devices         |
+| ------------------- | ----------------------- | -------------------------- |
+| armhf (ARM32)       | --config=elinux_armhf   | RPI3, RPI4 with 32 bit     |
+:                     :                         : Raspberry Pi OS            :
+| AArch64 (ARM64)     | --config=elinux_aarch64 | Coral, RPI4 with Ubuntu 64 |
+:                     :                         : bit                        :
+
+Note: The generated shared library requires glibc 2.28 or higher to run.
+
+The following instructions have been tested on Ubuntu 16.04.3 64-bit PC (AMD64)
+and TensorFlow devel docker image
+[tensorflow/tensorflow:devel](https://hub.docker.com/r/tensorflow/tensorflow/tags/).
+
+To cross compile TensorFlow Lite with Bazel, follow the steps:
+
+#### Step 1. Install Bazel
+
+Bazel is the primary build system for TensorFlow. Install the latest version of
+the [Bazel build system](https://bazel.build/versions/master/docs/install.html).
+
+**Note:** If you're using the TensorFlow Docker image, Bazel is already
+available.
+
+#### Step 2. Clone TensorFlow repository
+
+```sh
+git clone https://github.com/tensorflow/tensorflow.git tensorflow_src
+```
+
+**Note:** If you're using the TensorFlow Docker image, the repo is already
+provided in `/tensorflow_src/`.
+
+#### Step 3. Build ARM binary
+
+##### C library
+
+```bash
+bazel build --config=elinux_aarch64 -c opt //tensorflow/lite/c:libtensorflowlite_c.so
+```
+
+You can find a shared library in:
+`bazel-bin/tensorflow/lite/c/libtensorflowlite_c.so`.
+
+**Note:** Use `elinux_armhf` for
+[32bit ARM hard float](https://wiki.debian.org/ArmHardFloatPort) build.
+
+Check
+[TensorFlow Lite C API](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/c/README.md)
+page for the detail.
+
+##### C++ library
+
+```bash
+bazel build --config=elinux_aarch64 -c opt //tensorflow/lite:libtensorflowlite.so
+```
+
+You can find a shared library in:
+`bazel-bin/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 and
+Abseil.
+
+##### Etc
+
+You can also build other Bazel targets with the toolchain. Here are some useful
+targets.
+
+*   //tensorflow/lite/tools/benchmark:benchmark_model
+*   //tensorflow/lite/examples/label_image:label_image