chore: import upstream snapshot with attribution

tensorflow/python/compiler/tensorrt/README.md

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+# Using TensorRT in TensorFlow (TF-TRT)
+
+Note: Starting from v.2.18.0, TensorFlow doesn't support TensorRT.
+
+This module provides necessary bindings and introduces `TRTEngineOp` operator
+that wraps a subgraph in TensorRT. This module is under active development.
+
+## Installing TF-TRT
+
+Currently TensorFlow nightly builds include TF-TRT by default, which means you
+don't need to install TF-TRT separately. You can pull the latest TF containers
+from docker hub or install the latest TF pip package to get access to the latest
+TF-TRT.
+
+If you want to use TF-TRT on NVIDIA Jetson platform, you can find the download
+links for the relevant TensorFlow pip packages here:
+https://docs.nvidia.com/deeplearning/dgx/index.html#installing-frameworks-for-jetson
+
+## Installing TensorRT
+
+In order to make use of TF-TRT, you will need a local installation of TensorRT.
+Installation instructions for compatibility with TensorFlow are provided on the
+[TensorFlow GPU support](https://www.tensorflow.org/install/gpu) guide.
+
+## Examples
+
+You can find example scripts for running inference on deep learning models in
+this repository: https://github.com/tensorflow/tensorrt
+
+We have used these examples to verify the accuracy and performance of TF-TRT.
+For more information see
+[Verified Models](https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html#verified-models).
+
+## Documentation
+
+[TF-TRT documentation](https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html)
+gives an overview of the supported functionalities, provides tutorials and
+verified models, explains best practices with troubleshooting guides.
+
+## Tests
+
+TF-TRT includes both Python tests and C++ unit tests. Most of Python tests are
+located in the test directory and they can be executed using `bazel test` or
+directly with the Python command. Most of the C++ unit tests are used to test
+the conversion functions that convert each TF op to a number of TensorRT layers.
+
+## Compilation
+
+In order to compile the module, you need to have a local TensorRT installation
+(libnvinfer.so and respective include files). During the configuration step,
+TensorRT should be enabled and installation path should be set. If installed
+through package managers (deb,rpm), configure script should find the necessary
+components from the system automatically. If installed from tar packages, user
+has to set path to location where the library is installed during configuration.
+
+```shell
+bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package
+bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/
+```
+